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ORCIDXiangnan He 0001
Person information
- affiliation: University of Science and Technology of China, School of Information Science and Technology, Hefei, China
- affiliation (PhD 2016): National University of Singapore, School of Computing, Singapore
Other persons with the same name
- Xiangnan He 0002 — Fudan University, Shanghai, China
- Xiangnan He 0003 — Southern University of Science and Technology, Shenzhen, China
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2020 – today
- 2024
[j84]Jiajia Chen, Jiancan Wu, Jiawei Chen, Xin Xin, Yong Li, Xiangnan He:
How graph convolutions amplify popularity bias for recommendation? Frontiers Comput. Sci. 18(5): 185603 (2024)- [j83]Shuo Wang
, Jinda Lu, Haiyang Xu, Yanbin Hao, Xiangnan He:
Feature Mixture on Pre-Trained Model for Few-Shot Learning. IEEE Trans. Image Process. 33: 4104-4115 (2024) - [j82]Yongduo Sui, Wenyu Mao, Shuyao Wang, Xiang Wang, Jiancan Wu, Xiangnan He, Tat-Seng Chua:
Enhancing Out-of-distribution Generalization on Graphs via Causal Attention Learning. ACM Trans. Knowl. Discov. Data 18(5): 127:1-127:24 (2024) - [j81]Xinyuan Zhu, Yang Zhang, Fuli Feng, Xun Yang, Dingxian Wang, Xiangnan He:
Mitigating Hidden Confounding Effects for Causal Recommendation. IEEE Trans. Knowl. Data Eng. 36(9): 4794-4805 (2024) - [j80]Yuan Gao, Jinghan Li, Xiang Wang, Xiangnan He, Huamin Feng, Yongdong Zhang:
Revisiting Attack-Caused Structural Distribution Shift in Graph Anomaly Detection. IEEE Trans. Knowl. Data Eng. 36(9): 4849-4861 (2024) - [j79]Weijian Chen, Yixin Cao, Fuli Feng, Xiangnan He, Yongdong Zhang:
HoGRN: Explainable Sparse Knowledge Graph Completion via High-Order Graph Reasoning Network. IEEE Trans. Knowl. Data Eng. 36(12): 8462-8475 (2024) - [j78]Sihao Ding, Fuli Feng, Xiangnan He, Yong Liao, Jun Shi, Yongdong Zhang:
Causal Incremental Graph Convolution for Recommender System Retraining. IEEE Trans. Neural Networks Learn. Syst. 35(4): 4718-4728 (2024) - [j77]Xun Deng, Fuli Feng, Xiang Wang, Xiangnan He, Hanwang Zhang, Tat-Seng Chua:
Learning to Double-Check Model Prediction From a Causal Perspective. IEEE Trans. Neural Networks Learn. Syst. 35(4): 5054-5063 (2024) - [j76]Chongming Gao, Shiqi Wang, Shijun Li, Jiawei Chen, Xiangnan He, Wenqiang Lei, Biao Li, Yuan Zhang, Peng Jiang:
CIRS: Bursting Filter Bubbles by Counterfactual Interactive Recommender System. ACM Trans. Inf. Syst. 42(1): 14:1-14:27 (2024) - [j75]Chen Gao, Yu Zheng, Wenjie Wang, Fuli Feng, Xiangnan He, Yong Li:
Causal Inference in Recommender Systems: A Survey and Future Directions. ACM Trans. Inf. Syst. 42(4): 88:1-88:32 (2024) - [c205]Jiayi Liao, Xu Chen, Qiang Fu, Lun Du, Xiangnan He, Xiang Wang, Shi Han, Dongmei Zhang:
Text-to-Image Generation for Abstract Concepts. AAAI 2024: 3360-3368 - [c204]Xingyu Zhu, Shuo Wang, Jinda Lu, Yanbin Hao, Haifeng Liu, Xiangnan He:
Boosting Few-Shot Learning via Attentive Feature Regularization. AAAI 2024: 7793-7801 - [c203]Yang Zhang, Keqin Bao, Ming Yan, Wenjie Wang, Fuli Feng, Xiangnan He:
Text-like Encoding of Collaborative Information in Large Language Models for Recommendation. ACL (1) 2024: 9181-9191 - [c202]Tianhao Shi, Yang Zhang, Zhijian Xu, Chong Chen, Fuli Feng, Xiangnan He, Qi Tian:
Preliminary Study on Incremental Learning for Large Language Model-based Recommender Systems. CIKM 2024: 4051-4055 - [c201]Zhicai Wang, Longhui Wei, Tan Wang, Heyu Chen, Yanbin Hao, Xiang Wang, Xiangnan He, Qi Tian:
Enhance Image Classification via Inter-Class Image Mixup with Diffusion Model. CVPR 2024: 17223-17233 - [c200]Sihang Li, Zhiyuan Liu, Yanchen Luo, Xiang Wang, Xiangnan He, Kenji Kawaguchi, Tat-Seng Chua, Qi Tian:
Towards 3D Molecule-Text Interpretation in Language Models. ICLR 2024 - [c199]Haoxuan Li, Chunyuan Zheng, Sihao Ding, Peng Wu, Zhi Geng, Fuli Feng, Xiangnan He:
Be Aware of the Neighborhood Effect: Modeling Selection Bias under Interference. ICLR 2024 - [c198]Xun Deng, Junlong Liu, Han Zhong, Fuli Feng, Chen Shen, Xiangnan He, Jieping Ye, Zheng Wang:
A3S: A General Active Clustering Method with Pairwise Constraints. ICML 2024 - [c197]Tianhao Shi, Yang Zhang, Jizhi Zhang, Fuli Feng, Xiangnan He:
Fair Recommendations with Limited Sensitive Attributes: A Distributionally Robust Optimization Approach. SIGIR 2024: 448-457 - [c196]Zihao Zhao, Yi Jing, Fuli Feng, Jiancan Wu, Chongming Gao, Xiangnan He:
Leave No Patient Behind: Enhancing Medication Recommendation for Rare Disease Patients. SIGIR 2024: 533-542 - [c195]Yiyan Xu, Wenjie Wang, Fuli Feng, Yunshan Ma, Jizhi Zhang, Xiangnan He:
Diffusion Models for Generative Outfit Recommendation. SIGIR 2024: 1350-1359 - [c194]Jiayi Liao, Sihang Li, Zhengyi Yang, Jiancan Wu, Yancheng Yuan, Xiang Wang, Xiangnan He:
LLaRA: Large Language-Recommendation Assistant. SIGIR 2024: 1785-1795 - [c193]Wentao Shi, Xiangnan He, Yang Zhang, Chongming Gao, Xinyue Li, Jizhi Zhang, Qifan Wang, Fuli Feng:
Large Language Models are Learnable Planners for Long-Term Recommendation. SIGIR 2024: 1893-1903 - [c192]Junfeng Fang, Xinglin Li, Yongduo Sui, Yuan Gao, Guibin Zhang, Kun Wang, Xiang Wang, Xiangnan He:
EXGC: Bridging Efficiency and Explainability in Graph Condensation. WWW 2024: 721-732 - [c191]Shuxian Bi, Wenjie Wang, Hang Pan, Fuli Feng, Xiangnan He:
Proactive Recommendation with Iterative Preference Guidance. WWW (Companion Volume) 2024: 871-874 - [c190]Jizhi Zhang, Keqin Bao, Yang Zhang, Wenjie Wang, Fuli Feng, Xiangnan He:
Large Language Models for Recommendation: Progresses and Future Directions. WWW (Companion Volume) 2024: 1268-1271 - [c189]Chen Gao, Fengli Xu, Xu Chen, Xiang Wang, Xiangnan He, Yong Li:
Simulating Human Society with Large Language Model Agents: City, Social Media, and Economic System. WWW (Companion Volume) 2024: 1290-1293 - [c188]Wenjie Wang, Yang Zhang, Xinyu Lin, Fuli Feng, Weiwen Liu, Yong Liu, Xiangyu Zhao, Wayne Xin Zhao, Yang Song, Xiangnan He:
The 2nd Workshop on Recommendation with Generative Models. WWW (Companion Volume) 2024: 1715-1718 - [c187]Wentao Shi, Chenxu Wang, Fuli Feng, Yang Zhang, Wenjie Wang, Junkang Wu, Xiangnan He:
Lower-Left Partial AUC: An Effective and Efficient Optimization Metric for Recommendation. WWW 2024: 3253-3264 - [c186]Meng Jiang, Keqin Bao, Jizhi Zhang, Wenjie Wang, Zhengyi Yang, Fuli Feng, Xiangnan He:
Item-side Fairness of Large Language Model-based Recommendation System. WWW 2024: 4717-4726 - [i184]Sihang Li, Zhiyuan Liu, Yanchen Luo, Xiang Wang, Xiangnan He, Kenji Kawaguchi, Tat-Seng Chua, Qi Tian:
Towards 3D Molecule-Text Interpretation in Language Models. CoRR abs/2401.13923 (2024) - [i183]Yuan Gao, Xiang Wang, Xiangnan He, Zhenguang Liu, Huamin Feng, Yongdong Zhang:
Alleviating Structural Distribution Shift in Graph Anomaly Detection. CoRR abs/2401.14155 (2024) - [i182]Junfeng Fang, Xinglin Li, Yongduo Sui, Yuan Gao, Guibin Zhang, Kun Wang, Xiang Wang, Xiangnan He:
EXGC: Bridging Efficiency and Explainability in Graph Condensation. CoRR abs/2402.05962 (2024) - [i181]Meng Jiang, Keqin Bao, Jizhi Zhang, Wenjie Wang, Zhengyi Yang, Fuli Feng, Xiangnan He:
Item-side Fairness of Large Language Model-based Recommendation System. CoRR abs/2402.15215 (2024) - [i180]Yiyan Xu, Wenjie Wang, Fuli Feng, Yunshan Ma, Jizhi Zhang, Xiangnan He:
DiFashion: Towards Personalized Outfit Generation and Recommendation. CoRR abs/2402.17279 (2024) - [i179]Wentao Shi, Xiangnan He, Yang Zhang, Chongming Gao, Xinyue Li, Jizhi Zhang, Qifan Wang, Fuli Feng:
Enhancing Long-Term Recommendation with Bi-level Learnable Large Language Model Planning. CoRR abs/2403.00843 (2024) - [i178]Wentao Shi, Chenxu Wang, Fuli Feng, Yang Zhang, Wenjie Wang, Junkang Wu, Xiangnan He:
Lower-Left Partial AUC: An Effective and Efficient Optimization Metric for Recommendation. CoRR abs/2403.00844 (2024) - [i177]Wenjie Wang, Yang Zhang, Xinyu Lin, Fuli Feng, Weiwen Liu, Yong Liu, Xiangyu Zhao, Wayne Xin Zhao, Yang Song, Xiangnan He:
The 2nd Workshop on Recommendation with Generative Models. CoRR abs/2403.04399 (2024) - [i176]Shuxian Bi, Wenjie Wang, Hang Pan, Fuli Feng, Xiangnan He:
Proactive Recommendation with Iterative Preference Guidance. CoRR abs/2403.07571 (2024) - [i175]Xingyu Zhu, Shuo Wang, Jinda Lu, Yanbin Hao, Haifeng Liu, Xiangnan He:
Boosting Few-Shot Learning via Attentive Feature Regularization. CoRR abs/2403.17025 (2024) - [i174]Zihao Zhao, Yi Jing, Fuli Feng, Jiancan Wu, Chongming Gao, Xiangnan He:
Leave No Patient Behind: Enhancing Medication Recommendation for Rare Disease Patients. CoRR abs/2403.17745 (2024) - [i173]Zhicai Wang, Longhui Wei, Tan Wang, Heyu Chen, Yanbin Hao, Xiang Wang, Xiangnan He, Qi Tian:
Enhance Image Classification via Inter-Class Image Mixup with Diffusion Model. CoRR abs/2403.19600 (2024) - [i172]Zhiyu Hu, Yang Zhang, Minghao Xiao, Wenjie Wang, Fuli Feng, Xiangnan He:
Exact and Efficient Unlearning for Large Language Model-based Recommendation. CoRR abs/2404.10327 (2024) - [i171]Yongqi Li, Xinyu Lin, Wenjie Wang, Fuli Feng, Liang Pang, Wenjie Li, Liqiang Nie, Xiangnan He, Tat-Seng Chua:
A Survey of Generative Search and Recommendation in the Era of Large Language Models. CoRR abs/2404.16924 (2024) - [i170]Haoxuan Li, Chunyuan Zheng, Sihao Ding, Peng Wu, Zhi Geng, Fuli Feng, Xiangnan He:
Be Aware of the Neighborhood Effect: Modeling Selection Bias under Interference. CoRR abs/2404.19620 (2024) - [i169]Tianhao Shi, Yang Zhang, Jizhi Zhang, Fuli Feng, Xiangnan He:
Fair Recommendations with Limited Sensitive Attributes: A Distributionally Robust Optimization Approach. CoRR abs/2405.01063 (2024) - [i168]Yang Zhang, Keqin Bao, Ming Yang, Wenjie Wang, Fuli Feng, Xiangnan He:
Text-like Encoding of Collaborative Information in Large Language Models for Recommendation. CoRR abs/2406.03210 (2024) - [i167]Junkang Wu, Yuexiang Xie, Zhengyi Yang, Jiancan Wu, Jiawei Chen, Jinyang Gao, Bolin Ding, Xiang Wang, Xiangnan He:
Towards Robust Alignment of Language Models: Distributionally Robustifying Direct Preference Optimization. CoRR abs/2407.07880 (2024) - [i166]Junkang Wu, Yuexiang Xie, Zhengyi Yang, Jiancan Wu, Jinyang Gao, Bolin Ding, Xiang Wang, Xiangnan He:
β-DPO: Direct Preference Optimization with Dynamic β. CoRR abs/2407.08639 (2024) - [i165]Xun Deng, Junlong Liu, Han Zhong, Fuli Feng, Chen Shen, Xiangnan He, Jieping Ye, Zheng Wang:
A3S: A General Active Clustering Method with Pairwise Constraints. CoRR abs/2407.10196 (2024) - [i164]Wenyu Mao, Jiancan Wu, Weijian Chen, Chongming Gao, Xiang Wang, Xiangnan He:
Reinforced Prompt Personalization for Recommendation with Large Language Models. CoRR abs/2407.17115 (2024) - [i163]Xiaoyu Kong, Jiancan Wu, An Zhang, Leheng Sheng, Hui Lin, Xiang Wang, Xiangnan He:
Customizing Language Models with Instance-wise LoRA for Sequential Recommendation. CoRR abs/2408.10159 (2024) - [i162]Hang Pan, Shuxian Bi, Wenjie Wang, Haoxuan Li, Peng Wu, Fuli Feng, Xiangnan He:
Proactive Recommendation in Social Networks: Steering User Interest via Neighbor Influence. CoRR abs/2409.08934 (2024) - [i161]Changyi Xiao, Xiangnan He, Yixin Cao:
Knowledge Graph Embedding by Normalizing Flows. CoRR abs/2409.19977 (2024) - [i160]Junfeng Fang, Houcheng Jiang, Kun Wang, Yunshan Ma, Xiang Wang, Xiangnan He, Tat-Seng Chua:
AlphaEdit: Null-Space Constrained Knowledge Editing for Language Models. CoRR abs/2410.02355 (2024) - [i159]Zhaochun Ren, Xiangnan He, Dawei Yin, Maarten de Rijke:
Information Discovery in e-Commerce. CoRR abs/2410.05763 (2024) - [i158]Junkang Wu, Xue Wang, Zhengyi Yang, Jiancan Wu, Jinyang Gao, Bolin Ding, Xiang Wang, Xiangnan He:
α-DPO: Adaptive Reward Margin is What Direct Preference Optimization Needs. CoRR abs/2410.10148 (2024) - [i157]Jiayi Liao, Xiangnan He, Ruobing Xie, Jiancan Wu, Yancheng Yuan, Xingwu Sun, Zhanhui Kang, Xiang Wang:
RosePO: Aligning LLM-based Recommenders with Human Values. CoRR abs/2410.12519 (2024) - [i156]Yiyan Xu, Wenjie Wang, Yang Zhang, Tang Biao, Peng Yan, Fuli Feng, Xiangnan He:
Personalized Image Generation with Large Multimodal Models. CoRR abs/2410.14170 (2024) - [i155]Keqin Bao, Ming Yang, Yang Zhang, Jizhi Zhang, Wenjie Wang, Fuli Feng, Xiangnan He:
Real-Time Personalization for LLM-based Recommendation with Customized In-Context Learning. CoRR abs/2410.23136 (2024) - 2023
- [j74]Qingyao Ai, Ting Bai, Zhao Cao, Yi Chang, Jiawei Chen, Zhumin Chen, Zhiyong Cheng, Shoubin Dong, Zhicheng Dou, Fuli Feng, Shen Gao, Jiafeng Guo, Xiangnan He, Yanyan Lan, Chenliang Li, Yiqun Liu, Ziyu Lyu, Weizhi Ma, Jun Ma, Zhaochun Ren, Pengjie Ren, Zhiqiang Wang, Mingwen Wang, Ji-Rong Wen, Le Wu, Xin Xin, Jun Xu, Dawei Yin, Peng Zhang, Fan Zhang, Weinan Zhang, Min Zhang, Xiaofei Zhu:
Information Retrieval meets Large Language Models: A strategic report from Chinese IR community. AI Open 4: 80-90 (2023) - [j73]Zeyu Yang, Jizhi Zhang, Fuli Feng, Chongming Gao, Qifan Wang, Xiangnan He:
Interactive active learning for fairness with partial group label. AI Open 4: 175-182 (2023) - [j72]Yuan Gao, Xiang Wang, Xiangnan He, Huamin Feng, Yong-Dong Zhang:
Rumor detection with self-supervised learning on texts and social graph. Frontiers Comput. Sci. 17(4): 174611 (2023) - [j71]Xiang Wang, Yingxin Wu, An Zhang, Fuli Feng, Xiangnan He, Tat-Seng Chua:
Reinforced Causal Explainer for Graph Neural Networks. IEEE Trans. Pattern Anal. Mach. Intell. 45(2): 2297-2309 (2023) - [j70]Chenxu Wang, Fuli Feng, Yang Zhang, Qifan Wang, Xunhan Hu, Xiangnan He:
Rethinking Missing Data: Aleatoric Uncertainty-Aware Recommendation. IEEE Trans. Big Data 9(6): 1607-1619 (2023) - [j69]Kang Liu, Feng Xue, Xiangnan He, Dan Guo, Richang Hong:
Joint Multi-Grained Popularity-Aware Graph Convolution Collaborative Filtering for Recommendation. IEEE Trans. Comput. Soc. Syst. 10(1): 72-83 (2023) - [j68]Jintang Li, Tao Xie, Liang Chen, Fenfang Xie, Xiangnan He, Zibin Zheng:
Adversarial Attack on Large Scale Graph. IEEE Trans. Knowl. Data Eng. 35(1): 82-95 (2023) - [j67]Fuli Feng, Xiangnan He, Hanwang Zhang, Tat-Seng Chua:
Cross-GCN: Enhancing Graph Convolutional Network with $k$k-Order Feature Interactions. IEEE Trans. Knowl. Data Eng. 35(1): 225-236 (2023) - [j66]Yu Zheng, Chen Gao, Xiangnan He, Depeng Jin, Yong Li:
Incorporating Price into Recommendation With Graph Convolutional Networks. IEEE Trans. Knowl. Data Eng. 35(2): 1609-1623 (2023) - [j65]Jianxin Chang, Chen Gao, Xiangnan He, Depeng Jin, Yong Li:
Bundle Recommendation and Generation With Graph Neural Networks. IEEE Trans. Knowl. Data Eng. 35(3): 2326-2340 (2023) - [j64]Lei Chen, Fajie Yuan, Jiaxi Yang, Xiangnan He, Chengming Li, Min Yang:
User-Specific Adaptive Fine-Tuning for Cross-Domain Recommendations. IEEE Trans. Knowl. Data Eng. 35(3): 3239-3252 (2023) - [j63]Weijian Chen, Fuli Feng, Qifan Wang, Xiangnan He, Chonggang Song, Guohui Ling, Yongdong Zhang:
CatGCN: Graph Convolutional Networks With Categorical Node Features. IEEE Trans. Knowl. Data Eng. 35(4): 3500-3511 (2023) - [j62]Le Wu, Xiangnan He, Xiang Wang, Kun Zhang, Meng Wang:
A Survey on Accuracy-Oriented Neural Recommendation: From Collaborative Filtering to Information-Rich Recommendation. IEEE Trans. Knowl. Data Eng. 35(5): 4425-4445 (2023) - [j61]Jiajia Chen, Xin Xin, Xianfeng Liang, Xiangnan He, Jun Liu:
GDSRec: Graph-Based Decentralized Collaborative Filtering for Social Recommendation. IEEE Trans. Knowl. Data Eng. 35(5): 4813-4824 (2023) - [j60]Zihao Zhao, Jiawei Chen, Sheng Zhou, Xiangnan He, Xuezhi Cao, Fuzheng Zhang, Wei Wu:
Popularity Bias is not Always Evil: Disentangling Benign and Harmful Bias for Recommendation. IEEE Trans. Knowl. Data Eng. 35(10): 9920-9931 (2023) - [j59]Bin Wu, Xiangnan He, Qi Zhang, Meng Wang, Yangdong Ye:
GCRec: Graph-Augmented Capsule Network for Next-Item Recommendation. IEEE Trans. Neural Networks Learn. Syst. 34(12): 10164-10177 (2023) - [j58]Yuyue Zhao, Xiang Wang, Jiawei Chen, Yashen Wang, Wei Tang, Xiangnan He, Haiyong Xie:
Time-aware Path Reasoning on Knowledge Graph for Recommendation. ACM Trans. Inf. Syst. 41(2): 26:1-26:26 (2023) - [j57]Xiangnan He, Yang Zhang, Fuli Feng, Chonggang Song, Lingling Yi, Guohui Ling, Yongdong Zhang:
Addressing Confounding Feature Issue for Causal Recommendation. ACM Trans. Inf. Syst. 41(3): 53:1-53:23 (2023) - [j56]Jiawei Chen, Hande Dong, Xiang Wang, Fuli Feng, Meng Wang, Xiangnan He:
Bias and Debias in Recommender System: A Survey and Future Directions. ACM Trans. Inf. Syst. 41(3): 67:1-67:39 (2023) - [j55]Honglei Zhang, Fangyuan Luo, Jun Wu, Xiangnan He, Yidong Li:
LightFR: Lightweight Federated Recommendation with Privacy-preserving Matrix Factorization. ACM Trans. Inf. Syst. 41(4): 90:1-90:28 (2023) - [j54]Shuo Wang, Huixia Ben, Yanbin Hao, Xiangnan He, Meng Wang:
Boosting Hyperspectral Image Classification with Dual Hierarchical Learning. ACM Trans. Multim. Comput. Commun. Appl. 19(1): 21:1-21:19 (2023) - [j53]Chen Gao, Yu Zheng, Nian Li, Yinfeng Li, Yingrong Qin, Jinghua Piao, Yuhan Quan, Jianxin Chang, Depeng Jin, Xiangnan He, Yong Li:
A Survey of Graph Neural Networks for Recommender Systems: Challenges, Methods, and Directions. Trans. Recomm. Syst. 1(1): 1-51 (2023) - [j52]Bin Wu, Xiangnan He, Le Wu, Xue Zhang, Yangdong Ye:
Graph-Augmented Co-Attention Model for Socio-Sequential Recommendation. IEEE Trans. Syst. Man Cybern. Syst. 53(7): 4039-4051 (2023) - [c185]Changyi Xiao, Xiangnan He, Yixin Cao:
Knowledge Graph Embedding by Normalizing Flows. AAAI 2023: 4756-4764 - [c184]Xun Deng, Wenjie Wang, Fuli Feng, Hanwang Zhang, Xiangnan He, Yong Liao:
Counterfactual Active Learning for Out-of-Distribution Generalization. ACL (1) 2023: 11362-11377 - [c183]Jiarui Yu, Haoran Li, Yanbin Hao, Jinmeng Wu, Tong Xu, Shuo Wang, Xiangnan He:
How Can Contrastive Pre-training Benefit Audio-Visual Segmentation? A Study from Supervised and Zero-shot Perspectives. BMVC 2023: 367-374 - [c182]Wenjie Wang, Yong Liu, Yang Zhang, Weiwen Liu, Fuli Feng, Xiangnan He, Aixin Sun:
The 1st Workshop on Recommendation with Generative Models. CIKM 2023: 5300-5303 - [c181]Zhicai Wang, Yanbin Hao, Tingting Mu, Ouxiang Li, Shuo Wang, Xiangnan He:
Bi-Directional Distribution Alignment for Transductive Zero-Shot Learning. CVPR 2023: 19893-19902 - [c180]Boyi Deng, Wenjie Wang, Fuli Feng, Yang Deng, Qifan Wang, Xiangnan He:
Attack Prompt Generation for Red Teaming and Defending Large Language Models. EMNLP (Findings) 2023: 2176-2189 - [c179]Meng Jiang, Yang Zhang, Yuan Gao, Yansong Wang, Fuli Feng, Xiangnan He:
LightMIRM: Light Meta-learned Invariant Risk Minimization for Trustworthy Loan Default Prediction. ICDE 2023: 3494-3507 - [c178]Yang Liu, Liang Chen, Xiangnan He, Jiaying Peng, Zibin Zheng, Jie Tang:
Modelling High-Order Social Relations for Item Recommendation (Extended Abstract). ICDE 2023: 3821-3822 - [c177]Bin Wu, Xiangnan He, Yu Chen, Liqiang Nie, Kai Zheng, Yangdong Ye:
Modeling Product's Visual and Functional Characteristics for Recommender Systems (Extended Abstract). ICDE 2023: 3837-3838 - [c176]Hang Pan, Jiawei Chen, Fuli Feng, Wentao Shi, Junkang Wu, Xiangnan He:
Discriminative-Invariant Representation Learning for Unbiased Recommendation. IJCAI 2023: 2270-2278 - [c175]Jiarui Yu, Haoran Li, Yanbin Hao, Bin Zhu, Tong Xu, Xiangnan He:
CgT-GAN: CLIP-guided Text GAN for Image Captioning. ACM Multimedia 2023: 2252-2263 - [c174]Jinda Lu, Shuo Wang, Xinyu Zhang, Yanbin Hao, Xiangnan He:
Semantic-based Selection, Synthesis, and Supervision for Few-shot Learning. ACM Multimedia 2023: 3569-3578 - [c173]Junfeng Fang, Wei Liu, Yuan Gao, Zemin Liu, An Zhang, Xiang Wang, Xiangnan He:
Evaluating Post-hoc Explanations for Graph Neural Networks via Robustness Analysis. NeurIPS 2023 - [c172]Jinqiu Jin, Haoxuan Li, Fuli Feng, Sihao Ding, Peng Wu, Xiangnan He:
Fairly Recommending with Social Attributes: A Flexible and Controllable Optimization Approach. NeurIPS 2023 - [c171]Haoxuan Li, Kunhan Wu, Chunyuan Zheng, Yanghao Xiao, Hao Wang, Zhi Geng, Fuli Feng, Xiangnan He, Peng Wu:
Removing Hidden Confounding in Recommendation: A Unified Multi-Task Learning Approach. NeurIPS 2023 - [c170]Yongduo Sui, Qitian Wu, Jiancan Wu, Qing Cui, Longfei Li, Jun Zhou, Xiang Wang, Xiangnan He:
Unleashing the Power of Graph Data Augmentation on Covariate Distribution Shift. NeurIPS 2023 - [c169]Junkang Wu, Jiawei Chen, Jiancan Wu, Wentao Shi, Xiang Wang, Xiangnan He:
Understanding Contrastive Learning via Distributionally Robust Optimization. NeurIPS 2023 - [c168]Zhengyi Yang, Jiancan Wu, Zhicai Wang, Xiang Wang, Yancheng Yuan, Xiangnan He:
Generate What You Prefer: Reshaping Sequential Recommendation via Guided Diffusion. NeurIPS 2023 - [c167]Changsheng Wang, Jianbai Ye, Wenjie Wang, Chongming Gao, Fuli Feng, Xiangnan He:
RecAD: Towards A Unified Library for Recommender Attack and Defense. RecSys 2023: 234-244 - [c166]Haoxuan Li, Taojun Hu, Zetong Xiong, Chunyuan Zheng, Fuli Feng, Xiangnan He, Xiao-Hua Zhou:
ADRNet: A Generalized Collaborative Filtering Framework Combining Clinical and Non-Clinical Data for Adverse Drug Reaction Prediction. RecSys 2023: 682-687 - [c165]Jizhi Zhang, Keqin Bao, Yang Zhang, Wenjie Wang, Fuli Feng, Xiangnan He:
Is ChatGPT Fair for Recommendation? Evaluating Fairness in Large Language Model Recommendation. RecSys 2023: 993-999 - [c164]Keqin Bao, Jizhi Zhang, Yang Zhang, Wenjie Wang, Fuli Feng, Xiangnan He:
TALLRec: An Effective and Efficient Tuning Framework to Align Large Language Model with Recommendation. RecSys 2023: 1007-1014 - [c163]Chongming Gao, Kexin Huang, Jiawei Chen, Yuan Zhang, Biao Li, Peng Jiang, Shiqi Wang, Zhong Zhang, Xiangnan He:
Alleviating Matthew Effect of Offline Reinforcement Learning in Interactive Recommendation. SIGIR 2023: 238-248 - [c162]Zhengyi Yang, Xiangnan He, Jizhi Zhang, Jiancan Wu, Xin Xin, Jiawei Chen, Xiang Wang:
A Generic Learning Framework for Sequential Recommendation with Distribution Shifts. SIGIR 2023: 331-340 - [c161]Wenjie Wang, Yiyan Xu, Fuli Feng, Xinyu Lin, Xiangnan He, Tat-Seng Chua:
Diffusion Recommender Model. SIGIR 2023: 832-841 - [c160]Yang Zhang, Tianhao Shi, Fuli Feng, Wenjie Wang, Dingxian Wang, Xiangnan He, Yongdong Zhang:
Reformulating CTR Prediction: Learning Invariant Feature Interactions for Recommendation. SIGIR 2023: 1386-1395 - [c159]Wenjie Wang, Yang Zhang, Haoxuan Li, Peng Wu, Fuli Feng, Xiangnan He:
Causal Recommendation: Progresses and Future Directions. SIGIR 2023: 3432-3435 - [c158]Keqin Bao, Jizhi Zhang, Yang Zhang, Wenjie Wang, Fuli Feng, Xiangnan He:
Large Language Models for Recommendation: Progresses and Future Directions. SIGIR-AP 2023: 306-309 - [c157]Yuan Gao, Xiang Wang, Xiangnan He, Zhenguang Liu, Huamin Feng, Yongdong Zhang:
Alleviating Structural Distribution Shift in Graph Anomaly Detection. WSDM 2023: 357-365 - [c156]Junfeng Fang, Xiang Wang, An Zhang, Zemin Liu, Xiangnan He, Tat-Seng Chua:
Cooperative Explanations of Graph Neural Networks. WSDM 2023: 616-624 - [c155]Gang Chen, Jiawei Chen, Fuli Feng, Sheng Zhou, Xiangnan He:
Unbiased Knowledge Distillation for Recommendation. WSDM 2023: 976-984 - [c154]Jiancan Wu, Yi Yang, Yuchun Qian, Yongduo Sui, Xiang Wang, Xiangnan He:
GIF: A General Graph Unlearning Strategy via Influence Function. WWW 2023: 651-661 - [c153]Wentao Shi, Jiawei Chen, Fuli Feng, Jizhi Zhang, Junkang Wu, Chongming Gao, Xiangnan He:
On the Theories Behind Hard Negative Sampling for Recommendation. WWW 2023: 812-822 - [c152]Jiawei Chen, Junkang Wu, Jiancan Wu, Xuezhi Cao, Sheng Zhou, Xiangnan He:
Adap-τ : Adaptively Modulating Embedding Magnitude for Recommendation. WWW 2023: 1085-1096 - [c151]Yuan Gao, Xiang Wang, Xiangnan He, Zhenguang Liu, Huamin Feng, Yongdong Zhang:
Addressing Heterophily in Graph Anomaly Detection: A Perspective of Graph Spectrum. WWW 2023: 1528-1538 - [i154]Wentao Shi, Jiawei Chen, Fuli Feng, Jizhi Zhang, Junkang Wu, Chongming Gao, Xiangnan He:
On the Theories Behind Hard Negative Sampling for Recommendation. CoRR abs/2302.03472 (2023) - [i153]Wentao Shi, Junkang Wu, Xuezhi Cao, Jiawei Chen, Wenqiang Lei, Wei Wu, Xiangnan He:
FFHR: Fully and Flexible Hyperbolic Representation for Knowledge Graph Completion. CoRR abs/2302.04088 (2023) - [i152]Minqi Jiang, Chaochuan Hou, Ao Zheng, Xiyang Hu, Songqiao Han, Hailiang Huang, Xiangnan He, Philip S. Yu, Yue Zhao:
Weakly Supervised Anomaly Detection: A Survey. CoRR abs/2302.04549 (2023) - [i151]Jiawei Chen, Junkang Wu, Jiancan Wu, Sheng Zhou, Xuezhi Cao, Xiangnan He:
Adap-tau: Adaptively Modulating Embedding Magnitude for Recommendation. CoRR abs/2302.04775 (2023) - [i150]Keqin Bao, Yu Wan, Dayiheng Liu, Baosong Yang, Wenqiang Lei, Xiangnan He, Derek F. Wong, Jun Xie:
Towards Fine-Grained Information: Identifying the Type and Location of Translation Errors. CoRR abs/2302.08975 (2023) - [i149]Zhicai Wang, Yanbin Hao, Tingting Mu, Ouxiang Li, Shuo Wang, Xiangnan He:
Bi-directional Distribution Alignment for Transductive Zero-Shot Learning. CoRR abs/2303.08698 (2023) - [i148]Jiancan Wu, Yi Yang, Yuchun Qian, Yongduo Sui, Xiang Wang, Xiangnan He:
GIF: A General Graph Unlearning Strategy via Influence Function. CoRR abs/2304.02835 (2023) - [i147]Wenjie Wang, Xinyu Lin, Fuli Feng, Xiangnan He, Tat-Seng Chua:
Generative Recommendation: Towards Next-generation Recommender Paradigm. CoRR abs/2304.03516 (2023) - [i146]Wenjie Wang, Yiyan Xu, Fuli Feng, Xinyu Lin, Xiangnan He, Tat-Seng Chua:
Diffusion Recommender Model. CoRR abs/2304.04971 (2023) - [i145]Yang Zhang, Tianhao Shi, Fuli Feng, Wenjie Wang, Dingxian Wang, Xiangnan He, Yongdong Zhang:
Reformulating CTR Prediction: Learning Invariant Feature Interactions for Recommendation. CoRR abs/2304.13643 (2023) - [i144]Keqin Bao, Jizhi Zhang, Yang Zhang, Wenjie Wang, Fuli Feng, Xiangnan He:
TALLRec: An Effective and Efficient Tuning Framework to Align Large Language Model with Recommendation. CoRR abs/2305.00447 (2023) - [i143]Jizhi Zhang, Keqin Bao, Yang Zhang, Wenjie Wang, Fuli Feng, Xiangnan He:
Is ChatGPT Fair for Recommendation? Evaluating Fairness in Large Language Model Recommendation. CoRR abs/2305.07609 (2023) - [i142]Jiajia Chen, Jiancan Wu, Jiawei Chen, Xin Xin, Yong Li, Xiangnan He:
How Graph Convolutions Amplify Popularity Bias for Recommendation? CoRR abs/2305.14886 (2023) - [i141]Gangyi Zhang, Chongming Gao, Wenqiang Lei, Xiaojie Guo, Shijun Li, Lingfei Wu, Hongshen Chen, Zhuozhi Ding, Sulong Xu, Xiangnan He:
Embracing Uncertainty: Adaptive Vague Preference Policy Learning for Multi-round Conversational Recommendation. CoRR abs/2306.04487 (2023) - [i140]Yang Zhang, Zhiyu Hu, Yimeng Bai, Fuli Feng, Jiancan Wu, Qifan Wang, Xiangnan He:
Recommendation Unlearning via Influence Function. CoRR abs/2307.02147 (2023) - [i139]Chongming Gao, Kexin Huang, Jiawei Chen, Yuan Zhang, Biao Li, Peng Jiang, Shiqi Wang, Zhong Zhang, Xiangnan He:
Alleviating Matthew Effect of Offline Reinforcement Learning in Interactive Recommendation. CoRR abs/2307.04571 (2023) - [i138]Qingyao Ai, Ting Bai, Zhao Cao, Yi Chang, Jiawei Chen, Zhumin Chen, Zhiyong Cheng, Shoubin Dong, Zhicheng Dou, Fuli Feng, Shen Gao, Jiafeng Guo, Xiangnan He, Yanyan Lan, Chenliang Li, Yiqun Liu, Ziyu Lyu, Weizhi Ma, Jun Ma, Zhaochun Ren, Pengjie Ren, Zhiqiang Wang, Mingwen Wang, Ji-Rong Wen, Le Wu, Xin Xin, Jun Xu, Dawei Yin, Peng Zhang, Fan Zhang, Weinan Zhang, Min Zhang, Xiaofei Zhu:
Information Retrieval Meets Large Language Models: A Strategic Report from Chinese IR Community. CoRR abs/2307.09751 (2023) - [i137]Haoxuan Li, Taojun Hu, Zetong Xiong, Chunyuan Zheng, Fuli Feng, Xiangnan He, Xiao-Hua Zhou:
ADRNet: A Generalized Collaborative Filtering Framework Combining Clinical and Non-Clinical Data for Adverse Drug Reaction Prediction. CoRR abs/2308.02571 (2023) - [i136]Keqin Bao, Jizhi Zhang, Wenjie Wang, Yang Zhang, Zhengyi Yang, Yancheng Luo, Fuli Feng, Xiangnan He, Qi Tian:
A Bi-Step Grounding Paradigm for Large Language Models in Recommendation Systems. CoRR abs/2308.08434 (2023) - [i135]Jiarui Yu, Haoran Li, Yanbin Hao, Bin Zhu, Tong Xu, Xiangnan He:
CgT-GAN: CLIP-guided Text GAN for Image Captioning. CoRR abs/2308.12045 (2023) - [i134]Yu Wang, Xin Xin, Zaiqiao Meng, Xiangnan He, Joemon M. Jose, Fuli Feng:
Label Denoising through Cross-Model Agreement. CoRR abs/2308.13976 (2023) - [i133]Changsheng Wang, Jianbai Ye, Wenjie Wang, Chongming Gao, Fuli Feng, Xiangnan He:
RecAD: Towards A Unified Library for Recommender Attack and Defense. CoRR abs/2309.04884 (2023) - [i132]Yi Tan, Zhaofan Qiu, Yanbin Hao, Ting Yao, Xiangnan He, Tao Mei:
Selective Volume Mixup for Video Action Recognition. CoRR abs/2309.09534 (2023) - [i131]Jiayi Liao, Xu Chen, Qiang Fu, Lun Du, Xiangnan He, Xiang Wang, Shi Han, Dongmei Zhang:
Text-to-Image Generation for Abstract Concepts. CoRR abs/2309.14623 (2023) - [i130]Yongxin Ni, Yu Cheng, Xiangyan Liu, Junchen Fu, Youhua Li, Xiangnan He, Yongfeng Zhang, Fajie Yuan:
A Content-Driven Micro-Video Recommendation Dataset at Scale. CoRR abs/2309.15379 (2023) - [i129]Junkang Wu, Jiawei Chen, Jiancan Wu, Wentao Shi, Xiang Wang, Xiangnan He:
Understanding Contrastive Learning via Distributionally Robust Optimization. CoRR abs/2310.11048 (2023) - [i128]Boyi Deng, Wenjie Wang, Fuli Feng, Yang Deng, Qifan Wang, Xiangnan He:
Attack Prompt Generation for Red Teaming and Defending Large Language Models. CoRR abs/2310.12505 (2023) - [i127]Chengpeng Li, Zhengyi Yang, Jizhi Zhang, Jiancan Wu, Dingxian Wang, Xiangnan He, Xiang Wang:
Model-enhanced Contrastive Reinforcement Learning for Sequential Recommendation. CoRR abs/2310.16566 (2023) - [i126]Yang Zhang, Fuli Feng, Jizhi Zhang, Keqin Bao, Qifan Wang, Xiangnan He:
CoLLM: Integrating Collaborative Embeddings into Large Language Models for Recommendation. CoRR abs/2310.19488 (2023) - [i125]Zhengyi Yang, Jiancan Wu, Zhicai Wang, Xiang Wang, Yancheng Yuan, Xiangnan He:
Generate What You Prefer: Reshaping Sequential Recommendation via Guided Diffusion. CoRR abs/2310.20453 (2023) - [i124]Zhengyi Yang, Jiancan Wu, Yanchen Luo, Jizhi Zhang, Yancheng Yuan, An Zhang, Xiang Wang, Xiangnan He:
Large Language Model Can Interpret Latent Space of Sequential Recommender. CoRR abs/2310.20487 (2023) - [i123]Fangzhou Song, Bin Zhu, Yanbin Hao, Shuo Wang, Xiangnan He:
CAR: Consolidation, Augmentation and Regulation for Recipe Retrieval. CoRR abs/2312.04763 (2023) - [i122]Tianhao Shi, Yang Zhang, Zhijian Xu, Chong Chen, Fuli Feng, Xiangnan He, Qi Tian:
Preliminary Study on Incremental Learning for Large Language Model-based Recommender Systems. CoRR abs/2312.15599 (2023) - 2022
- [j51]Jiancan Wu, Xiangnan He, Xiang Wang, Qifan Wang, Weijian Chen, Jianxun Lian, Xing Xie:
Graph convolution machine for context-aware recommender system. Frontiers Comput. Sci. 16(6): 166614 (2022) - [j50]Yanfang Wang, Yongduo Sui, Xiang Wang, Zhenguang Liu, Xiangnan He:
Exploring lottery ticket hypothesis in media recommender systems. Int. J. Intell. Syst. 37(5): 3006-3024 (2022) - [j49]Richang Hong, Daqing Liu, Xiaoyu Mo, Xiangnan He, Hanwang Zhang:
Learning to Compose and Reason with Language Tree Structures for Visual Grounding. IEEE Trans. Pattern Anal. Mach. Intell. 44(2): 684-696 (2022) - [j48]Yuanyuan Jin, Wendi Ji, Wei Zhang, Xiangnan He, Xinyu Wang, Xiaoling Wang:
A KG-Enhanced Multi-Graph Neural Network for Attentive Herb Recommendation. IEEE ACM Trans. Comput. Biol. Bioinform. 19(5): 2560-2571 (2022) - [j47]Yanbin Hao, Shuo Wang, Pei Cao, Xinjian Gao, Tong Xu, Jinmeng Wu, Xiangnan He:
Attention in Attention: Modeling Context Correlation for Efficient Video Classification. IEEE Trans. Circuits Syst. Video Technol. 32(10): 7120-7132 (2022) - [j46]Yanbin Hao, Shuo Wang, Yi Tan, Xiangnan He, Zhenguang Liu, Meng Wang:
Spatio-Temporal Collaborative Module for Efficient Action Recognition. IEEE Trans. Image Process. 31: 7279-7291 (2022) - [j45]Chen Gao, Yong Li, Fuli Feng, Xiangning Chen, Kai Zhao, Xiangnan He, Depeng Jin:
Cross-domain Recommendation with Bridge-Item Embeddings. ACM Trans. Knowl. Discov. Data 16(1): 2:1-2:23 (2022) - [j44]Ming Gao, Xiangnan He, Leihui Chen, Tingting Liu, Jinglin Zhang, Aoying Zhou:
Learning Vertex Representations for Bipartite Networks. IEEE Trans. Knowl. Data Eng. 34(1): 379-393 (2022) - [j43]Bin Wu, Xiangnan He, Yun Chen, Liqiang Nie, Kai Zheng, Yangdong Ye:
Modeling Product's Visual and Functional Characteristics for Recommender Systems. IEEE Trans. Knowl. Data Eng. 34(3): 1330-1343 (2022) - [j42]Meng Wang, Weijie Fu, Xiangnan He, Shijie Hao, Xindong Wu:
A Survey on Large-Scale Machine Learning. IEEE Trans. Knowl. Data Eng. 34(6): 2574-2594 (2022) - [j41]Yang Liu, Liang Chen, Xiangnan He, Jiaying Peng, Zibin Zheng, Jie Tang:
Modelling High-Order Social Relations for Item Recommendation. IEEE Trans. Knowl. Data Eng. 34(9): 4385-4397 (2022) - [j40]Yinwei Wei, Xiang Wang, Xiangnan He, Liqiang Nie, Yong Rui, Tat-Seng Chua:
Hierarchical User Intent Graph Network for Multimedia Recommendation. IEEE Trans. Multim. 24: 2701-2712 (2022) - [j39]Xiangnan He, Zhaochun Ren, Emine Yilmaz, Marc Najork, Tat-Seng Chua:
Graph Technologies for User Modeling and Recommendation: Introduction to the Special Issue - Part 1. ACM Trans. Inf. Syst. 40(2): 21:1-21:5 (2022) - [j38]Xiangnan He, Zhaochun Ren, Emine Yilmaz, Marc Najork, Tat-Seng Chua:
Introduction to the Special Section on Graph Technologies for User Modeling and Recommendation, Part 2. ACM Trans. Inf. Syst. 40(3): 42:1-42:5 (2022) - [c150]Moxin Li, Fuli Feng, Hanwang Zhang, Xiangnan He, Fengbin Zhu, Tat-Seng Chua:
Learning to Imagine: Integrating Counterfactual Thinking in Neural Discrete Reasoning. ACL (1) 2022: 57-69 - [c149]Chongming Gao, Shijun Li, Wenqiang Lei, Jiawei Chen, Biao Li, Peng Jiang, Xiangnan He, Jiaxin Mao, Tat-Seng Chua:
KuaiRec: A Fully-observed Dataset and Insights for Evaluating Recommender Systems. CIKM 2022: 540-550 - [c148]Chongming Gao, Shijun Li, Yuan Zhang, Jiawei Chen, Biao Li, Wenqiang Lei, Peng Jiang, Xiangnan He:
KuaiRand: An Unbiased Sequential Recommendation Dataset with Randomly Exposed Videos. CIKM 2022: 3953-3957 - [c147]Chao Huang, Lianghao Xia, Xiang Wang, Xiangnan He, Dawei Yin:
Self-Supervised Learning for Recommendation. CIKM 2022: 5136-5139 - [c146]Yanbin Hao, Hao Zhang, Chong-Wah Ngo, Xiangnan He:
Group Contextualization for Video Recognition. CVPR 2022: 918-928 - [c145]Chuhan Wu, Fangzhao Wu, Xiangnan He, Yongfeng Huang:
DebiasGAN: Eliminating Position Bias in News Recommendation with Adversarial Learning. EMNLP (Findings) 2022: 2933-2938 - [c144]Yingxin Wu, Xiang Wang, An Zhang, Xiangnan He, Tat-Seng Chua:
Discovering Invariant Rationales for Graph Neural Networks. ICLR 2022 - [c143]Sihang Li, Xiang Wang, An Zhang, Yingxin Wu, Xiangnan He, Tat-Seng Chua:
Let Invariant Rationale Discovery Inspire Graph Contrastive Learning. ICML 2022: 13052-13065 - [c142]Sihao Ding, Peng Wu, Fuli Feng, Yitong Wang, Xiangnan He, Yong Liao, Yongdong Zhang:
Addressing Unmeasured Confounder for Recommendation with Sensitivity Analysis. KDD 2022: 305-315 - [c141]Yongduo Sui, Xiang Wang, Jiancan Wu, Min Lin, Xiangnan He, Tat-Seng Chua:
Causal Attention for Interpretable and Generalizable Graph Classification. KDD 2022: 1696-1705 - [c140]Xiaoyu Du, Zike Wu, Fuli Feng, Xiangnan He, Jinhui Tang:
Invariant Representation Learning for Multimedia Recommendation. ACM Multimedia 2022: 619-628 - [c139]Yanbin Hao, Jingru Duan, Hao Zhang, Bin Zhu, Pengyuan Zhou, Xiangnan He:
Unsupervised Video Hashing with Multi-granularity Contextualization and Multi-structure Preservation. ACM Multimedia 2022: 3754-3763 - [c138]Shuo Wang, Xinyu Zhang, Yanbin Hao, Chengbing Wang, Xiangnan He:
Multi-directional Knowledge Transfer for Few-Shot Learning. ACM Multimedia 2022: 3993-4002 - [c137]Yi Tan, Yanbin Hao, Hao Zhang, Shuo Wang, Xiangnan He:
Hierarchical Hourglass Convolutional Network for Efficient Video Classification. ACM Multimedia 2022: 5880-5891 - [c136]Zhicai Wang, Yanbin Hao, Xingyu Gao, Hao Zhang, Shuo Wang, Tingting Mu, Xiangnan He:
Parameterization of Cross-token Relations with Relative Positional Encoding for Vision MLP. ACM Multimedia 2022: 6288-6299 - [c135]Sihao Ding, Fuli Feng, Xiangnan He, Jinqiu Jin, Wenjie Wang, Yong Liao, Yongdong Zhang:
Interpolative Distillation for Unifying Biased and Debiased Recommendation. SIGIR 2022: 40-49 - [c134]Chao Huang, Xiang Wang, Xiangnan He, Dawei Yin:
Self-Supervised Learning for Recommender System. SIGIR 2022: 3440-3443 - [c133]Keqin Bao, Yu Wan, Dayiheng Liu, Baosong Yang, Wenqiang Lei, Xiangnan He, Derek F. Wong, Jun Xie:
Alibaba-Translate China's Submission for WMT 2022 Quality Estimation Shared Task. WMT 2022: 597-605 - [c132]Chen Gao, Xiang Wang, Xiangnan He, Yong Li:
Graph Neural Networks for Recommender System. WSDM 2022: 1623-1625 - [c131]Dian Cheng, Jiawei Chen, Wenjun Peng, Wenqin Ye, Fuyu Lv, Tao Zhuang, Xiaoyi Zeng, Xiangnan He:
IHGNN: Interactive Hypergraph Neural Network for Personalized Product Search. WWW 2022: 256-265 - [c130]Riccardo Tommasini, Senjuti Basu Roy, Xuan Wang, Hongwei Wang, Heng Ji, Jiawei Han, Preslav Nakov, Giovanni Da San Martino, Firoj Alam, Markus Schedl, Elisabeth Lex, Akash Bharadwaj, Graham Cormode, Milan Dojchinovski, Jan Forberg, Johannes Frey, Pieter Bonte, Marco Balduini, Matteo Belcao, Emanuele Della Valle, Junliang Yu, Hongzhi Yin, Tong Chen, Haochen Liu, Yiqi Wang, Wenqi Fan, Xiaorui Liu, Jamell Dacon, Lingjuan Lye, Jiliang Tang, Aristides Gionis, Stefan Neumann, Bruno Ordozgoiti, Simon Razniewski, Hiba Arnaout, Shrestha Ghosh, Fabian M. Suchanek, Lingfei Wu, Yu Chen, Yunyao Li, Bang Liu, Filip Ilievski, Daniel Garijo, Hans Chalupsky, Pedro A. Szekely, Ilias Kanellos, Dimitris Sacharidis, Thanasis Vergoulis, Nurendra Choudhary, Nikhil Rao, Karthik Subbian, Srinivasan H. Sengamedu, Chandan K. Reddy, Friedhelm Victor, Bernhard Haslhofer, George Katsogiannis-Meimarakis, Georgia Koutrika, Shengmin Jin, Danai Koutra, Reza Zafarani, Yulia Tsvetkov, Vidhisha Balachandran, Sachin Kumar, Xiangyu Zhao, Bo Chen, Huifeng Guo, Yejing Wang, Ruiming Tang, Yang Zhang, Wenjie Wang, Peng Wu, Fuli Feng, Xiangnan He:
Accepted Tutorials at The Web Conference 2022. WWW (Companion Volume) 2022: 391-399 - [c129]Yu Wang, Xin Xin, Zaiqiao Meng, Joemon M. Jose, Fuli Feng, Xiangnan He:
Learning Robust Recommenders through Cross-Model Agreement. WWW 2022: 2015-2025 - [c128]Qi Wan, Xiangnan He, Xiang Wang, Jiancan Wu, Wei Guo, Ruiming Tang:
Cross Pairwise Ranking for Unbiased Item Recommendation. WWW 2022: 2370-2378 - [c127]Wenjie Wang, Xinyu Lin, Fuli Feng, Xiangnan He, Min Lin, Tat-Seng Chua:
Causal Representation Learning for Out-of-Distribution Recommendation. WWW 2022: 3562-3571 - [r1]Yashar Deldjoo, Markus Schedl, Balázs Hidasi, Yinwei Wei, Xiangnan He:
Multimedia Recommender Systems: Algorithms and Challenges. Recommender Systems Handbook 2022: 973-1014 - [i121]Jiancan Wu, Xiang Wang, Xingyu Gao, Jiawei Chen, Hongcheng Fu, Tianyu Qiu, Xiangnan He:
On the Effectiveness of Sampled Softmax Loss for Item Recommendation. CoRR abs/2201.02327 (2022) - [i120]Ying-Xin Wu, Xiang Wang, An Zhang, Xia Hu, Fuli Feng, Xiangnan He, Tat-Seng Chua:
Deconfounding to Explanation Evaluation in Graph Neural Networks. CoRR abs/2201.08802 (2022) - [i119]Ying-Xin Wu, Xiang Wang, An Zhang, Xiangnan He, Tat-Seng Chua:
Discovering Invariant Rationales for Graph Neural Networks. CoRR abs/2201.12872 (2022) - [i118]Dian Cheng, Jiawei Chen, Wenjun Peng, Wenqin Ye, Fuyu Lv, Tao Zhuang, Xiaoyi Zeng, Xiangnan He:
IHGNN: Interactive Hypergraph Neural Network for Personalized Product Search. CoRR abs/2202.04972 (2022) - [i117]Chongming Gao, Shijun Li, Wenqiang Lei, Biao Li, Peng Jiang, Jiawei Chen, Xiangnan He, Jiaxin Mao, Tat-Seng Chua:
KuaiRec: A Fully-observed Dataset for Recommender Systems. CoRR abs/2202.10842 (2022) - [i116]Yanbin Hao, Hao Zhang, Chong-Wah Ngo, Xiangnan He:
Group Contextualization for Video Recognition. CoRR abs/2203.09694 (2022) - [i115]Chongming Gao, Wenqiang Lei, Jiawei Chen, Shiqi Wang, Xiangnan He, Shijun Li, Biao Li, Yuan Zhang, Peng Jiang:
CIRS: Bursting Filter Bubbles by Counterfactual Interactive Recommender System. CoRR abs/2204.01266 (2022) - [i114]Yuan Gao, Xiang Wang, Xiangnan He, Huamin Feng, Yongdong Zhang:
Rumor Detection with Self-supervised Learning on Texts and Social Graph. CoRR abs/2204.08838 (2022) - [i113]Yanbin Hao, Shuo Wang, Pei Cao, Xinjian Gao, Tong Xu, Jinmeng Wu, Xiangnan He:
Attention in Attention: Modeling Context Correlation for Efficient Video Classification. CoRR abs/2204.09303 (2022) - [i112]Xiang Wang, Ying-Xin Wu, An Zhang, Fuli Feng, Xiangnan He, Tat-Seng Chua:
Reinforced Causal Explainer for Graph Neural Networks. CoRR abs/2204.11028 (2022) - [i111]Qi Wan, Xiangnan He, Xiang Wang, Jiancan Wu, Wei Guo, Ruiming Tang:
Cross Pairwise Ranking for Unbiased Item Recommendation. CoRR abs/2204.12176 (2022) - [i110]Xiangnan He, Yang Zhang, Fuli Feng, Chonggang Song, Lingling Yi, Guohui Ling, Yongdong Zhang:
Addressing Confounding Feature Issue for Causal Recommendation. CoRR abs/2205.06532 (2022) - [i109]Xinyuan Zhu, Yang Zhang, Fuli Feng, Xun Yang, Dingxian Wang, Xiangnan He:
Mitigating Hidden Confounding Effects for Causal Recommendation. CoRR abs/2205.07499 (2022) - [i108]Jiajia Chen, Xin Xin, Xianfeng Liang, Xiangnan He, Jun Liu:
GDSRec: Graph-Based Decentralized Collaborative Filtering for Social Recommendation. CoRR abs/2205.09948 (2022) - [i107]Yu Wang, An Zhang, Xiang Wang, Xiangnan He, Tat-Seng Chua:
Differentiable Invariant Causal Discovery. CoRR abs/2205.15638 (2022) - [i106]Sihang Li, Xiang Wang, An Zhang, Yingxin Wu, Xiangnan He, Tat-Seng Chua:
Let Invariant Rationale Discovery Inspire Graph Contrastive Learning. CoRR abs/2206.07869 (2022) - [i105]Honglei Zhang, Fangyuan Luo, Jun Wu, Xiangnan He, Yidong Li:
LightFR: Lightweight Federated Recommendation with Privacy-preserving Matrix Factorization. CoRR abs/2206.11743 (2022) - [i104]Zhicai Wang, Yanbin Hao, Xingyu Gao, Hao Zhang, Shuo Wang, Tingting Mu, Xiangnan He:
Parameterization of Cross-Token Relations with Relative Positional Encoding for Vision MLP. CoRR abs/2207.07284 (2022) - [i103]Weijian Chen, Yixin Cao, Fuli Feng, Xiangnan He, Yongdong Zhang:
Explainable Sparse Knowledge Graph Completion via High-order Graph Reasoning Network. CoRR abs/2207.07503 (2022) - [i102]Chongming Gao, Shijun Li, Yuan Zhang, Jiawei Chen, Biao Li, Wenqiang Lei, Peng Jiang, Xiangnan He:
KuaiRand: An Unbiased Sequential Recommendation Dataset with Randomly Exposed Videos. CoRR abs/2208.08696 (2022) - [i101]Chen Gao, Yu Zheng, Wenjie Wang, Fuli Feng, Xiangnan He, Yong Li:
Causal Inference in Recommender Systems: A Survey and Future Directions. CoRR abs/2208.12397 (2022) - [i100]Chenxu Wang, Fuli Feng, Yang Zhang, Qifan Wang, Xunhan Hu, Xiangnan He:
Rethinking Missing Data: Aleatoric Uncertainty-Aware Recommendation. CoRR abs/2209.11679 (2022) - [i99]Kang Liu, Feng Xue, Xiangnan He, Dan Guo, Richang Hong:
Joint Multi-grained Popularity-aware Graph Convolution Collaborative Filtering for Recommendation. CoRR abs/2210.04614 (2022) - [i98]Keqin Bao, Yu Wan, Dayiheng Liu, Baosong Yang, Wenqiang Lei, Xiangnan He, Derek F. Wong, Jun Xie:
Alibaba-Translate China's Submission for WMT 2022 Quality Estimation Shared Task. CoRR abs/2210.10049 (2022) - [i97]Yongduo Sui, Xiang Wang, Jiancan Wu, An Zhang, Xiangnan He:
Adversarial Causal Augmentation for Graph Covariate Shift. CoRR abs/2211.02843 (2022) - [i96]Gang Chen, Jiawei Chen, Fuli Feng, Sheng Zhou, Xiangnan He:
Unbiased Knowledge Distillation for Recommendation. CoRR abs/2211.14729 (2022) - 2021
- [j37]Chongming Gao, Wenqiang Lei, Xiangnan He, Maarten de Rijke, Tat-Seng Chua:
Advances and challenges in conversational recommender systems: A survey. AI Open 2: 100-126 (2021) - [j36]Shunyu Jiang, Fuli Feng, Weijian Chen, Xiang Li, Xiangnan He:
Structure-enhanced meta-learning for few-shot graph classification. AI Open 2: 160-167 (2021) - [j35]Yeon-Chang Lee, Taeho Kim, Jaeho Choi, Xiangnan He, Sang-Wook Kim:
M-BPR: A novel approach to improving BPR for recommendation with multi-type pair-wise preferences. Inf. Sci. 547: 255-270 (2021) - [j34]Jingtao Ding, Guanghui Yu, Xiangnan He, Fuli Feng, Yong Li, Depeng Jin:
Sampler Design for Bayesian Personalized Ranking by Leveraging View Data. IEEE Trans. Knowl. Data Eng. 33(2): 667-681 (2021) - [j33]Da Cao, Xiangnan He, Lianhai Miao, Guangyi Xiao, Hao Chen, Jia Xu:
Social-Enhanced Attentive Group Recommendation. IEEE Trans. Knowl. Data Eng. 33(3): 1195-1209 (2021) - [j32]Fuli Feng, Xiangnan He, Jie Tang, Tat-Seng Chua:
Graph Adversarial Training: Dynamically Regularizing Based on Graph Structure. IEEE Trans. Knowl. Data Eng. 33(6): 2493-2504 (2021) - [j31]Chen Gao, Xiangnan He, Dahua Gan, Xiangning Chen, Fuli Feng, Yong Li, Tat-Seng Chua, Lina Yao, Yang Song, Depeng Jin:
Learning to Recommend With Multiple Cascading Behaviors. IEEE Trans. Knowl. Data Eng. 33(6): 2588-2601 (2021) - [j30]Yaoyao Liu, Qianru Sun, Xiangnan He, An-An Liu, Yuting Su, Tat-Seng Chua:
Generating Face Images With Attributes for Free. IEEE Trans. Neural Networks Learn. Syst. 32(6): 2733-2743 (2021) - [j29]Jiawei Chen, Chengquan Jiang, Can Wang, Sheng Zhou, Yan Feng, Chun Chen, Martin Ester, Xiangnan He:
CoSam: An Efficient Collaborative Adaptive Sampler for Recommendation. ACM Trans. Inf. Syst. 39(3): 34:1-34:24 (2021) - [j28]Shijun Li, Wenqiang Lei, Qingyun Wu, Xiangnan He, Peng Jiang, Tat-Seng Chua:
Seamlessly Unifying Attributes and Items: Conversational Recommendation for Cold-start Users. ACM Trans. Inf. Syst. 39(4): 40:1-40:29 (2021) - [j27]Tong Xu, Peilun Zhou, Linkang Hu, Xiangnan He, Yao Hu, Enhong Chen:
Socializing the Videos: A Multimodal Approach for Social Relation Recognition. ACM Trans. Multim. Comput. Commun. Appl. 17(1): 23:1-23:23 (2021) - [j26]Wenhui Yu, Xiangnan He, Jian Pei, Xu Chen, Li Xiong, Jinfei Liu, Zheng Qin:
Visually aware recommendation with aesthetic features. VLDB J. 30(4): 495-513 (2021) - [c126]Fuli Feng, Jizhi Zhang, Xiangnan He, Hanwang Zhang, Tat-Seng Chua:
Empowering Language Understanding with Counterfactual Reasoning. ACL/IJCNLP (Findings) 2021: 2226-2236 - [c125]Daizong Ding, Mi Zhang, Hanrui Wang, Xudong Pan, Min Yang, Xiangnan He:
A Deep Learning Framework for Self-evolving Hierarchical Community Detection. CIKM 2021: 372-381 - [c124]Junkang Wu, Wentao Shi, Xuezhi Cao, Jiawei Chen, Wenqiang Lei, Fuzheng Zhang, Wei Wu, Xiangnan He:
DisenKGAT: Knowledge Graph Embedding with Disentangled Graph Attention Network. CIKM 2021: 2140-2149 - [c123]Fuli Feng, Xiang Wang, Xiangnan He, Ritchie Ng, Tat-Seng Chua:
Time horizon-aware modeling of financial texts for stock price prediction. ICAIF 2021: 51:1-51:8 - [c122]Na Li, Renyu Zhu, Xiaoxu Zhou, Xiangnan He, Wenyuan Cai, Ming Gao, Aoying Zhou:
On Disambiguating Authors: Collaboration Network Reconstruction in a Bottom-up Manner. ICDE 2021: 888-899 - [c121]Shoujin Wang, Liang Hu, Yan Wang, Xiangnan He, Quan Z. Sheng, Mehmet A. Orgun, Longbing Cao, Francesco Ricci, Philip S. Yu:
Graph Learning based Recommender Systems: A Review. IJCAI 2021: 4644-4652 - [c120]Wenjie Wang, Fuli Feng, Xiangnan He, Xiang Wang, Tat-Seng Chua:
Deconfounded Recommendation for Alleviating Bias Amplification. KDD 2021: 1717-1725 - [c119]Tianxin Wei, Fuli Feng, Jiawei Chen, Ziwei Wu, Jinfeng Yi, Xiangnan He:
Model-Agnostic Counterfactual Reasoning for Eliminating Popularity Bias in Recommender System. KDD 2021: 1791-1800 - [c118]Yi Tan, Yanbin Hao, Xiangnan He, Yinwei Wei, Xun Yang:
Selective Dependency Aggregation for Action Classification. ACM Multimedia 2021: 592-601 - [c117]Xiang Wang, Ying-Xin Wu, An Zhang, Xiangnan He, Tat-Seng Chua:
Towards Multi-Grained Explainability for Graph Neural Networks. NeurIPS 2021: 18446-18458 - [c116]Jiawei Chen, Xiang Wang, Fuli Feng, Xiangnan He:
Bias Issues and Solutions in Recommender System: Tutorial on the RecSys 2021. RecSys 2021: 825-827 - [c115]Yang Zhang, Fuli Feng, Xiangnan He, Tianxin Wei, Chonggang Song, Guohui Ling, Yongdong Zhang:
Causal Intervention for Leveraging Popularity Bias in Recommendation. SIGIR 2021: 11-20 - [c114]Jiawei Chen, Hande Dong, Yang Qiu, Xiangnan He, Xin Xin, Liang Chen, Guli Lin, Keping Yang:
AutoDebias: Learning to Debias for Recommendation. SIGIR 2021: 21-30 - [c113]Jiancan Wu, Xiang Wang, Fuli Feng, Xiangnan He, Liang Chen, Jianxun Lian, Xing Xie:
Self-supervised Graph Learning for Recommendation. SIGIR 2021: 726-735 - [c112]Fuli Feng, Weiran Huang, Xiangnan He, Xin Xin, Qifan Wang, Tat-Seng Chua:
Should Graph Convolution Trust Neighbors? A Simple Causal Inference Method. SIGIR 2021: 1208-1218 - [c111]Wenjie Wang, Fuli Feng, Xiangnan He, Hanwang Zhang, Tat-Seng Chua:
Clicks can be Cheating: Counterfactual Recommendation for Mitigating Clickbait Issue. SIGIR 2021: 1288-1297 - [c110]Wenjie Wang, Fuli Feng, Xiangnan He, Liqiang Nie, Tat-Seng Chua:
Denoising Implicit Feedback for Recommendation. WSDM 2021: 373-381 - [c109]Xiang Wang, Tinglin Huang, Dingxian Wang, Yancheng Yuan, Zhenguang Liu, Xiangnan He, Tat-Seng Chua:
Learning Intents behind Interactions with Knowledge Graph for Recommendation. WWW 2021: 878-887 - [c108]Yu Zheng, Chen Gao, Xiang Li, Xiangnan He, Yong Li, Depeng Jin:
Disentangling User Interest and Conformity for Recommendation with Causal Embedding. WWW 2021: 2980-2991 - [c107]Hande Dong, Jiawei Chen, Fuli Feng, Xiangnan He, Shuxian Bi, Zhaolin Ding, Peng Cui:
On the Equivalence of Decoupled Graph Convolution Network and Label Propagation. WWW 2021: 3651-3662 - [i95]Chongming Gao, Wenqiang Lei, Xiangnan He, Maarten de Rijke, Tat-Seng Chua:
Advances and Challenges in Conversational Recommender Systems: A Survey. CoRR abs/2101.09459 (2021) - [i94]Xiang Wang, Tinglin Huang, Dingxian Wang, Yancheng Yuan, Zhenguang Liu, Xiangnan He, Tat-Seng Chua:
Learning Intents behind Interactions with Knowledge Graph for Recommendation. CoRR abs/2102.07057 (2021) - [i93]Shunyu Jiang, Fuli Feng, Weijian Chen, Xiang Li, Xiangnan He:
Structure-Enhanced Meta-Learning For Few-Shot Graph Classification. CoRR abs/2103.03547 (2021) - [i92]Le Wu, Xiangnan He, Xiang Wang, Kun Zhang, Meng Wang:
A Survey on Neural Recommendation: From Collaborative Filtering to Content and Context Enriched Recommendation. CoRR abs/2104.13030 (2021) - [i91]Jiawei Chen, Hande Dong, Yang Qiu, Xiangnan He, Xin Xin, Liang Chen, Guli Lin, Keping Yang:
AutoDebias: Learning to Debias for Recommendation. CoRR abs/2105.04170 (2021) - [i90]Yang Zhang, Fuli Feng, Xiangnan He, Tianxin Wei, Chonggang Song, Guohui Ling, Yongdong Zhang:
Causal Intervention for Leveraging Popularity Bias in Recommendation. CoRR abs/2105.06067 (2021) - [i89]Shoujin Wang, Liang Hu, Yan Wang, Xiangnan He, Quan Z. Sheng, Mehmet A. Orgun, Longbing Cao, Francesco Ricci, Philip S. Yu:
Graph Learning based Recommender Systems: A Review. CoRR abs/2105.06339 (2021) - [i88]Yu Wang, Xin Xin, Zaiqiao Meng, Xiangnan He, Joemon M. Jose, Fuli Feng:
Probabilistic and Variational Recommendation Denoising. CoRR abs/2105.09605 (2021) - [i87]Wenjie Wang, Fuli Feng, Xiangnan He, Xiang Wang, Tat-Seng Chua:
Deconfounded Recommendation for Alleviating Bias Amplification. CoRR abs/2105.10648 (2021) - [i86]Fuli Feng, Jizhi Zhang, Xiangnan He, Hanwang Zhang, Tat-Seng Chua:
Empowering Language Understanding with Counterfactual Reasoning. CoRR abs/2106.03046 (2021) - [i85]Lei Chen, Fajie Yuan, Jiaxi Yang, Xiangnan He, Chengming Li, Min Yang:
User-specific Adaptive Fine-tuning for Cross-domain Recommendations. CoRR abs/2106.07864 (2021) - [i84]Yanfang Wang, Yongduo Sui, Xiang Wang, Zhenguang Liu, Xiangnan He:
Exploring Lottery Ticket Hypothesis in Media Recommender Systems. CoRR abs/2108.00944 (2021) - [i83]Yuyue Zhao, Xiang Wang, Jiawei Chen, Wei Tang, Yashen Wang, Xiangnan He, Haiyong Xie:
Time-aware Path Reasoning on Knowledge Graph for Recommendation. CoRR abs/2108.02634 (2021) - [i82]Sihao Ding, Fuli Feng, Xiangnan He, Yong Liao, Jun Shi, Yongdong Zhang:
Causal Incremental Graph Convolution for Recommender System Retraining. CoRR abs/2108.06889 (2021) - [i81]Junkang Wu, Wentao Shi, Xuezhi Cao, Jiawei Chen, Wenqiang Lei, Fuzheng Zhang, Wei Wu, Xiangnan He:
DisenKGAT: Knowledge Graph Embedding with Disentangled Graph Attention Network. CoRR abs/2108.09628 (2021) - [i80]Zihao Zhao, Jiawei Chen, Sheng Zhou, Xiangnan He, Xuezhi Cao, Fuzheng Zhang, Wei Wu:
Popularity Bias Is Not Always Evil: Disentangling Benign and Harmful Bias for Recommendation. CoRR abs/2109.07946 (2021) - [i79]Chen Gao, Yu Zheng, Nian Li, Yinfeng Li, Yingrong Qin, Jinghua Piao, Yuhan Quan, Jianxin Chang, Depeng Jin, Xiangnan He, Yong Li:
Graph Neural Networks for Recommender Systems: Challenges, Methods, and Directions. CoRR abs/2109.12843 (2021) - [i78]Yinwei Wei, Xiang Wang, Xiangnan He, Liqiang Nie, Yong Rui, Tat-Seng Chua:
Hierarchical User Intent Graph Network forMultimedia Recommendation. CoRR abs/2110.14925 (2021) - [i77]Yinwei Wei, Xiang Wang, Liqiang Nie, Xiangnan He, Tat-Seng Chua:
GRCN: Graph-Refined Convolutional Network for Multimedia Recommendation with Implicit Feedback. CoRR abs/2111.02036 (2021) - [i76]Wenjie Wang, Fuli Feng, Xiangnan He, Liqiang Nie, Tat-Seng Chua:
Learning Robust Recommender from Noisy Implicit Feedback. CoRR abs/2112.01160 (2021) - [i75]Yongduo Sui, Xiang Wang, Jiancan Wu, Xiangnan He, Tat-Seng Chua:
Deconfounded Training for Graph Neural Networks. CoRR abs/2112.15089 (2021) - 2020
- [j25]Jun Xu, Xiangnan He, Hang Li:
Deep Learning for Matching in Search and Recommendation. Found. Trends Inf. Retr. 14(2-3): 102-288 (2020) - [j24]Zhulin Tao, Yinwei Wei, Xiang Wang, Xiangnan He, Xianglin Huang, Tat-Seng Chua:
MGAT: Multimodal Graph Attention Network for Recommendation. Inf. Process. Manag. 57(5): 102277 (2020) - [j23]Zhulin Tao, Xiang Wang, Xiangnan He, Xianglin Huang, Tat-Seng Chua:
HoAFM: A High-order Attentive Factorization Machine for CTR Prediction. Inf. Process. Manag. 57(6): 102076 (2020) - [j22]Da Cao, Ning Han, Hao Chen, Xiaochi Wei, Xiangnan He:
Video-based recipe retrieval. Inf. Sci. 514: 302-318 (2020) - [j21]Xiao Huang, Pengjie Ren, Zhaochun Ren, Fei Sun, Xiangnan He, Dawei Yin, Maarten de Rijke:
Report on the international workshop on natural language processing for recommendations (NLP4REC 2020) workshop held at WSDM 2020. SIGIR Forum 54(1): 6:1-6:5 (2020) - [j20]Bin Wu, Xiangnan He, Zhongchuan Sun, Liang Chen, Yangdong Ye:
ATM: An Attentive Translation Model for Next-Item Recommendation. IEEE Trans. Ind. Informatics 16(3): 1448-1459 (2020) - [j19]Jinhui Tang, Xiaoyu Du, Xiangnan He, Fajie Yuan, Qi Tian, Tat-Seng Chua:
Adversarial Training Towards Robust Multimedia Recommender System. IEEE Trans. Knowl. Data Eng. 32(5): 855-867 (2020) - [j18]Ye-Zheng Liu, Zhe Li, Chong Zhou, Yuanchun Jiang, Jianshan Sun, Meng Wang, Xiangnan He:
Generative Adversarial Active Learning for Unsupervised Outlier Detection. IEEE Trans. Knowl. Data Eng. 32(8): 1517-1528 (2020) - [j17]Xiaoyan Gao, Fuli Feng, Xiangnan He, Heyan Huang, Xinyu Guan, Chong Feng, Zhaoyan Ming, Tat-Seng Chua:
Hierarchical Attention Network for Visually-Aware Food Recommendation. IEEE Trans. Multim. 22(6): 1647-1659 (2020) - [j16]Feng Xue, Richang Hong, Xiangnan He, Jianwei Wang, Shengsheng Qian, Changsheng Xu:
Knowledge-Based Topic Model for Multi-Modal Social Event Analysis. IEEE Trans. Multim. 22(8): 2098-2110 (2020) - [j15]Xiangnan He, Jinhui Tang, Xiaoyu Du, Richang Hong, Tongwei Ren, Tat-Seng Chua:
Fast Matrix Factorization With Nonuniform Weights on Missing Data. IEEE Trans. Neural Networks Learn. Syst. 31(8): 2791-2804 (2020) - [j14]Jingtao Ding, Guanghui Yu, Yong Li, Xiangnan He, Depeng Jin:
Improving Implicit Recommender Systems with Auxiliary Data. ACM Trans. Inf. Syst. 38(1): 11:1-11:27 (2020) - [j13]Francesco Gelli, Tiberio Uricchio, Xiangnan He, Alberto Del Bimbo, Tat-Seng Chua:
Learning Visual Elements of Images for Discovery of Brand Posts. ACM Trans. Multim. Comput. Commun. Appl. 16(2): 56:1-56:21 (2020) - [c106]Haozhe Wu, Zhiyuan Hu, Jia Jia, Yaohua Bu, Xiangnan He, Tat-Seng Chua:
Mining Unfollow Behavior in Large-Scale Online Social Networks via Spatial-Temporal Interaction. AAAI 2020: 254-261 - [c105]Daizong Ding, Mi Zhang, Xudong Pan, Min Yang, Xiangnan He:
Improving the Robustness of Wasserstein Embedding by Adversarial PAC-Bayesian Learning. AAAI 2020: 3791-3800 - [c104]Yu Zheng, Chen Gao, Xiangnan He, Yong Li, Depeng Jin:
Price-aware Recommendation with Graph Convolutional Networks. ICDE 2020: 133-144 - [c103]Yuanyuan Jin, Wei Zhang, Xiangnan He, Xinyu Wang, Xiaoling Wang:
Syndrome-aware Herb Recommendation with Multi-Graph Convolution Network. ICDE 2020: 145-156 - [c102]Jun Kuang, Yixin Cao, Jianbing Zheng, Xiangnan He, Ming Gao, Aoying Zhou:
Improving Neural Relation Extraction with Implicit Mutual Relations. ICDE 2020: 1021-1032 - [c101]Daizong Ding, Mi Zhang, Xudong Pan, Min Yang, Xiangnan He:
Modeling Personalized Out-of-Town Distances in Location Recommendation. ICDM 2020: 112-121 - [c100]Tianxin Wei, Ziwei Wu, Ruirui Li, Ziniu Hu, Fuli Feng, Xiangnan He, Yizhou Sun, Wei Wang:
Fast Adaptation for Cold-start Collaborative Filtering with Meta-learning. ICDM 2020: 661-670 - [c99]Hongmin Zhu, Fuli Feng, Xiangnan He, Xiang Wang, Yan Li, Kai Zheng, Yongdong Zhang:
Bilinear Graph Neural Network with Neighbor Interactions. IJCAI 2020: 1452-1458 - [c98]Le Dai, Yu Yin, Chuan Qin, Tong Xu, Xiangnan He, Enhong Chen, Hui Xiong:
Enterprise Cooperation and Competition Analysis with a Sign-Oriented Preference Network. KDD 2020: 774-782 - [c97]Wenqiang Lei, Gangyi Zhang, Xiangnan He, Yisong Miao, Xiang Wang, Liang Chen, Tat-Seng Chua:
Interactive Path Reasoning on Graph for Conversational Recommendation. KDD 2020: 2073-2083 - [c96]Lei Meng, Fuli Feng, Xiangnan He, Xiaoyan Gao, Tat-Seng Chua:
Heterogeneous Fusion of Semantic and Collaborative Information for Visually-Aware Food Recommendation. ACM Multimedia 2020: 3460-3468 - [c95]Xiaoyu Du, Xiang Wang, Xiangnan He, Zechao Li, Jinhui Tang, Tat-Seng Chua:
How to Learn Item Representation for Cold-Start Multimedia Recommendation? ACM Multimedia 2020: 3469-3477 - [c94]Yinwei Wei, Xiang Wang, Liqiang Nie, Xiangnan He, Tat-Seng Chua:
Graph-Refined Convolutional Network for Multimedia Recommendation with Implicit Feedback. ACM Multimedia 2020: 3541-3549 - [c93]Da Cao, Yawen Zeng, Meng Liu, Xiangnan He, Meng Wang, Zheng Qin:
STRONG: Spatio-Temporal Reinforcement Learning for Cross-Modal Video Moment Localization. ACM Multimedia 2020: 4162-4170 - [c92]Xingchen Li, Xiang Wang, Xiangnan He, Long Chen, Jun Xiao, Tat-Seng Chua:
Hierarchical Fashion Graph Network for Personalized Outfit Recommendation. SIGIR 2020: 159-168 - [c91]Yang Liu, Xianzhuo Xia, Liang Chen, Xiangnan He, Carl Yang, Zibin Zheng:
Certifiable Robustness to Discrete Adversarial Perturbations for Factorization Machines. SIGIR 2020: 419-428 - [c90]Xiangnan He, Kuan Deng, Xiang Wang, Yan Li, Yong-Dong Zhang, Meng Wang:
LightGCN: Simplifying and Powering Graph Convolution Network for Recommendation. SIGIR 2020: 639-648 - [c89]Bowen Jin, Chen Gao, Xiangnan He, Depeng Jin, Yong Li:
Multi-behavior Recommendation with Graph Convolutional Networks. SIGIR 2020: 659-668 - [c88]Xiang Wang, Hongye Jin, An Zhang, Xiangnan He, Tong Xu, Tat-Seng Chua:
Disentangled Graph Collaborative Filtering. SIGIR 2020: 1001-1010 - [c87]Haoji Hu, Xiangnan He, Jinyang Gao, Zhi-Li Zhang:
Modeling Personalized Item Frequency Information for Next-basket Recommendation. SIGIR 2020: 1071-1080 - [c86]Fajie Yuan, Xiangnan He, Alexandros Karatzoglou, Liguang Zhang:
Parameter-Efficient Transfer from Sequential Behaviors for User Modeling and Recommendation. SIGIR 2020: 1469-1478 - [c85]Yang Zhang, Fuli Feng, Chenxu Wang, Xiangnan He, Meng Wang, Yan Li, Yongdong Zhang:
How to Retrain Recommender System?: A Sequential Meta-Learning Method. SIGIR 2020: 1479-1488 - [c84]Jianxin Chang, Chen Gao, Xiangnan He, Depeng Jin, Yong Li:
Bundle Recommendation with Graph Convolutional Networks. SIGIR 2020: 1673-1676 - [c83]Wenqiang Lei, Xiangnan He, Maarten de Rijke, Tat-Seng Chua:
Conversational Recommendation: Formulation, Methods, and Evaluation. SIGIR 2020: 2425-2428 - [c82]Fuli Feng, Cheng Luo, Xiangnan He, Yiqun Liu, Tat-Seng Chua:
FinIR 2020: The First Workshop on Information Retrieval in Finance. SIGIR 2020: 2451-2454 - [c81]Wenqiang Lei, Xiangnan He, Yisong Miao, Qingyun Wu, Richang Hong, Min-Yen Kan, Tat-Seng Chua:
Estimation-Action-Reflection: Towards Deep Interaction Between Conversational and Recommender Systems. WSDM 2020: 304-312 - [c80]Xiang Wang, Xiangnan He, Tat-Seng Chua:
Learning and Reasoning on Graph for Recommendation. WSDM 2020: 890-893 - [c79]Pengjie Ren, Zhaochun Ren, Fei Sun, Xiangnan He, Dawei Yin, Maarten de Rijke:
NLP4REC: The WSDM 2020 Workshop on Natural Language Processing for Recommendations. WSDM 2020: 907-908 - [c78]Xiang Wang, Yaokun Xu, Xiangnan He, Yixin Cao, Meng Wang, Tat-Seng Chua:
Reinforced Negative Sampling over Knowledge Graph for Recommendation. WWW 2020: 99-109 - [c77]Fajie Yuan, Xiangnan He, Haochuan Jiang, Guibing Guo, Jian Xiong, Zhezhao Xu, Yilin Xiong:
Future Data Helps Training: Modeling Future Contexts for Session-based Recommendation. WWW 2020: 303-313 - [i74]Fajie Yuan, Xiangnan He, Alexandros Karatzoglou, Liguang Zhang:
Parameter-Efficient Transfer from Sequential Behaviors for User Profiling and Recommendation. CoRR abs/2001.04253 (2020) - [i73]Jiancan Wu, Xiangnan He, Xiang Wang, Qifan Wang, Weijian Chen, Jianxun Lian, Xing Xie, Yongdong Zhang:
Graph Convolution Machine for Context-aware Recommender System. CoRR abs/2001.11402 (2020) - [i72]Xiangnan He, Kuan Deng, Xiang Wang, Yan Li, Yongdong Zhang, Meng Wang:
LightGCN: Simplifying and Powering Graph Convolution Network for Recommendation. CoRR abs/2002.02126 (2020) - [i71]Hongmin Zhu, Fuli Feng, Xiangnan He, Xiang Wang, Yan Li, Kai Zheng, Yongdong Zhang:
Bilinear Graph Neural Network with Node Interactions. CoRR abs/2002.03575 (2020) - [i70]Yuanyuan Jin, Wei Zhang, Xiangnan He, Xinyu Wang, Xiaoling Wang:
Syndrome-aware Herb Recommendation with Multi-Graph Convolution Network. CoRR abs/2002.08575 (2020) - [i69]Wenqiang Lei, Xiangnan He, Yisong Miao, Qingyun Wu, Richang Hong, Min-Yen Kan, Tat-Seng Chua:
Estimation-Action-Reflection: Towards Deep Interaction Between Conversational and Recommender Systems. CoRR abs/2002.09102 (2020) - [i68]Fuli Feng, Xiangnan He, Hanwang Zhang, Tat-Seng Chua:
Cross-GCN: Enhancing Graph Convolutional Network with k-Order Feature Interactions. CoRR abs/2003.02587 (2020) - [i67]Yu Zheng, Chen Gao, Xiangnan He, Yong Li, Depeng Jin:
Price-aware Recommendation with Graph Convolutional Networks. CoRR abs/2003.03975 (2020) - [i66]Liang Chen, Jintang Li, Jiaying Peng, Tao Xie, Zengxu Cao, Kun Xu, Xiangnan He, Zibin Zheng:
A Survey of Adversarial Learning on Graphs. CoRR abs/2003.05730 (2020) - [i65]Xiang Wang, Yaokun Xu, Xiangnan He, Yixin Cao, Meng Wang, Tat-Seng Chua:
Reinforced Negative Sampling over Knowledge Graph for Recommendation. CoRR abs/2003.05753 (2020) - [i64]Yang Liu, Liang Chen, Xiangnan He, Jiaying Peng, Zibin Zheng, Jie Tang:
Modelling High-Order Social Relations for Item Recommendation. CoRR abs/2003.10149 (2020) - [i63]Shoujin Wang, Liang Hu, Yan Wang, Xiangnan He, Quan Z. Sheng, Mehmet A. Orgun, Longbing Cao, Nan Wang, Francesco Ricci, Philip S. Yu:
Graph Learning Approaches to Recommender Systems: A Review. CoRR abs/2004.11718 (2020) - [i62]Jianxin Chang, Chen Gao, Xiangnan He, Yong Li, Depeng Jin:
Bundle Recommendation with Graph Convolutional Networks. CoRR abs/2005.03475 (2020) - [i61]Xingchen Li, Xiang Wang, Xiangnan He, Long Chen, Jun Xiao, Tat-Seng Chua:
Hierarchical Fashion Graph Network for Personalized Outfit Recommendation. CoRR abs/2005.12566 (2020) - [i60]Shijun Li, Wenqiang Lei, Qingyun Wu, Xiangnan He, Peng Jiang, Tat-Seng Chua:
Seamlessly Unifying Attributes and Items: Conversational Recommendation for Cold-Start Users. CoRR abs/2005.12979 (2020) - [i59]Yang Zhang, Fuli Feng, Chenxu Wang, Xiangnan He, Meng Wang, Yan Li, Yongdong Zhang:
How to Retrain Recommender System? A Sequential Meta-Learning Method. CoRR abs/2005.13258 (2020) - [i58]Haoji Hu, Xiangnan He, Jinyang Gao, Zhi-Li Zhang:
Modeling Personalized Item Frequency Information for Next-basket Recommendation. CoRR abs/2006.00556 (2020) - [i57]Wenjie Wang, Fuli Feng, Xiangnan He, Liqiang Nie, Tat-Seng Chua:
Denoising Implicit Feedback for Recommendation. CoRR abs/2006.04153 (2020) - [i56]Yu Zheng, Chen Gao, Xiang Li, Xiangnan He, Yong Li, Depeng Jin:
Disentangling User Interest and Popularity Bias for Recommendation with Causal Embedding. CoRR abs/2006.11011 (2020) - [i55]Hande Dong, Zhaolin Ding, Xiangnan He, Fuli Feng, Shuxian Bi:
Data Augmentation View on Graph Convolutional Network and the Proposal of Monte Carlo Graph Learning. CoRR abs/2006.13090 (2020) - [i54]Wenqiang Lei, Gangyi Zhang, Xiangnan He, Yisong Miao, Xiang Wang, Liang Chen, Tat-Seng Chua:
Interactive Path Reasoning on Graph for Conversational Recommendation. CoRR abs/2007.00194 (2020) - [i53]Xiang Wang, Hongye Jin, An Zhang, Xiangnan He, Tong Xu, Tat-Seng Chua:
Disentangled Graph Collaborative Filtering. CoRR abs/2007.01764 (2020) - [i52]Meng Wang, Weijie Fu, Xiangnan He, Shijie Hao, Xindong Wu:
A Survey on Large-scale Machine Learning. CoRR abs/2008.03911 (2020) - [i51]Jintang Li, Tao Xie, Liang Chen, Fenfang Xie, Xiangnan He, Zibin Zheng:
Adversarial Attack on Large Scale Graph. CoRR abs/2009.03488 (2020) - [i50]Weijian Chen, Fuli Feng, Qifan Wang, Xiangnan He, Chonggang Song, Guohui Ling, Yongdong Zhang:
CatGCN: Graph Convolutional Networks with Categorical Node Features. CoRR abs/2009.05303 (2020) - [i49]Wenjie Wang, Fuli Feng, Xiangnan He, Hanwang Zhang, Tat-Seng Chua:
"Click" Is Not Equal to "Like": Counterfactual Recommendation for Mitigating Clickbait Issue. CoRR abs/2009.09945 (2020) - [i48]Fajie Yuan, Guoxiao Zhang, Alexandros Karatzoglou, Xiangnan He, Joemon M. Jose, Beibei Kong, Yudong Li:
One Person, One Model, One World: Learning Continual User Representation without Forgetting. CoRR abs/2009.13724 (2020) - [i47]Jiawei Chen, Hande Dong, Xiang Wang, Fuli Feng, Meng Wang, Xiangnan He:
Bias and Debias in Recommender System: A Survey and Future Directions. CoRR abs/2010.03240 (2020) - [i46]Jiancan Wu, Xiang Wang, Fuli Feng, Xiangnan He, Liang Chen, Jianxun Lian, Xing Xie:
Self-supervised Graph Learning for Recommendation. CoRR abs/2010.10783 (2020) - [i45]Fuli Feng, Weiran Huang, Xin Xin, Xiangnan He, Tat-Seng Chua:
Should Graph Convolution Trust Neighbors? A Simple Causal Inference Method. CoRR abs/2010.11797 (2020) - [i44]Hande Dong, Jiawei Chen, Fuli Feng, Xiangnan He, Shuxian Bi, Zhaolin Ding, Peng Cui:
On the Equivalence of Decoupled Graph Convolution Network and Label Propagation. CoRR abs/2010.12408 (2020) - [i43]Tianxin Wei, Fuli Feng, Jiawei Chen, Chufeng Shi, Ziwei Wu, Jinfeng Yi, Xiangnan He:
Model-Agnostic Counterfactual Reasoning for Eliminating Popularity Bias in Recommender System. CoRR abs/2010.15363 (2020) - [i42]Jiawei Chen, Chengquan Jiang, Can Wang, Sheng Zhou, Yan Feng, Chun Chen, Martin Ester, Xiangnan He:
CoSam: An Efficient Collaborative Adaptive Sampler for Recommendation. CoRR abs/2011.07739 (2020) - [i41]Na Li, Renyu Zhu, Xiaoxu Zhou, Xiangnan He, Wenyuan Cai, Ming Gao, Aoying Zhou:
On Disambiguating Authors: Collaboration Network Reconstruction in a Bottom-up Manner. CoRR abs/2011.14333 (2020)
2010 – 2019
- 2019
- [j12]Xiangnan He, Zhenguang Liu, Hanwang Zhang, Chong-Wah Ngo, Svebor Karaman, Yongfeng Zhang:
Special issue on multimedia recommendation and multi-modal data analysis. Multim. Syst. 25(6): 591-592 (2019) - [j11]Fuli Feng, Xiangnan He, Xiang Wang, Cheng Luo, Yiqun Liu, Tat-Seng Chua:
Temporal Relational Ranking for Stock Prediction. ACM Trans. Inf. Syst. 37(2): 27:1-27:30 (2019) - [j10]Xinyu Guan, Zhiyong Cheng, Xiangnan He, Yongfeng Zhang, Zhibo Zhu, Qinke Peng, Tat-Seng Chua:
Attentive Aspect Modeling for Review-Aware Recommendation. ACM Trans. Inf. Syst. 37(3): 28:1-28:27 (2019) - [j9]Feng Xue, Xiangnan He, Xiang Wang, Jiandong Xu, Kai Liu, Richang Hong:
Deep Item-based Collaborative Filtering for Top-N Recommendation. ACM Trans. Inf. Syst. 37(3): 33:1-33:25 (2019) - [j8]Xiaoyu Du, Xiangnan He, Fajie Yuan, Jinhui Tang, Zhiguang Qin, Tat-Seng Chua:
Modeling Embedding Dimension Correlations via Convolutional Neural Collaborative Filtering. ACM Trans. Inf. Syst. 37(4): 47:1-47:22 (2019) - [c76]Xiang Wang, Dingxian Wang, Canran Xu, Xiangnan He, Yixin Cao, Tat-Seng Chua:
Explainable Reasoning over Knowledge Graphs for Recommendation. AAAI 2019: 5329-5336 - [c75]Xiangpeng Li, Jingkuan Song, Lianli Gao, Xianglong Liu, Wenbing Huang, Xiangnan He, Chuang Gan:
Beyond RNNs: Positional Self-Attention with Co-Attention for Video Question Answering. AAAI 2019: 8658-8665 - [c74]Xiang Wang, Xiangnan He, Tat-Seng Chua:
Learning and Reasoning on Graph for Recommendation. CIKM 2019: 2971-2972 - [c73]Long Chen, Hanwang Zhang, Jun Xiao, Xiangnan He, Shiliang Pu, Shih-Fu Chang:
Counterfactual Critic Multi-Agent Training for Scene Graph Generation. ICCV 2019: 4612-4622 - [c72]Chen Gao, Xiangnan He, Dahua Gan, Xiangning Chen, Fuli Feng, Yong Li, Tat-Seng Chua, Depeng Jin:
Neural Multi-task Recommendation from Multi-behavior Data. ICDE 2019: 1554-1557 - [c71]Liang Chen, Yang Liu, Xiangnan He, Lianli Gao, Zibin Zheng:
Matching User with Item Set: Collaborative Bundle Recommendation with Deep Attention Network. IJCAI 2019: 2095-2101 - [c70]Weijian Chen, Yulong Gu, Zhaochun Ren, Xiangnan He, Hongtao Xie, Tong Guo, Dawei Yin, Yongdong Zhang:
Semi-supervised User Profiling with Heterogeneous Graph Attention Networks. IJCAI 2019: 2116-2122 - [c69]Jingtao Ding, Yuhan Quan, Xiangnan He, Yong Li, Depeng Jin:
Reinforced Negative Sampling for Recommendation with Exposure Data. IJCAI 2019: 2230-2236 - [c68]Xin Xin, Bo Chen, Xiangnan He, Dong Wang, Yue Ding, Joemon M. Jose:
CFM: Convolutional Factorization Machines for Context-Aware Recommendation. IJCAI 2019: 3926-3932 - [c67]Fuli Feng, Huimin Chen, Xiangnan He, Ji Ding, Maosong Sun, Tat-Seng Chua:
Enhancing Stock Movement Prediction with Adversarial Training. IJCAI 2019: 5843-5849 - [c66]Xiang Wang, Xiangnan He, Yixin Cao, Meng Liu, Tat-Seng Chua:
KGAT: Knowledge Graph Attention Network for Recommendation. KDD 2019: 950-958 - [c65]Yihong Chen, Bei Chen, Xiangnan He, Chen Gao, Yong Li, Jian-Guang Lou, Yue Wang:
λOpt: Learn to Regularize Recommender Models in Finer Levels. KDD 2019: 978-986 - [c64]Daizong Ding, Mi Zhang, Xudong Pan, Min Yang, Xiangnan He:
Modeling Extreme Events in Time Series Prediction. KDD 2019: 1114-1122 - [c63]Haoji Hu, Xiangnan He:
Sets2Sets: Learning from Sequential Sets with Neural Networks. KDD 2019: 1491-1499 - [c62]Lixi Deng, Jingjing Chen, Qianru Sun, Xiangnan He, Sheng Tang, Zhaoyan Ming, Yongdong Zhang, Tat-Seng Chua:
Mixed-dish Recognition with Contextual Relation Networks. ACM Multimedia 2019: 112-120 - [c61]Yinwei Wei, Xiang Wang, Liqiang Nie, Xiangnan He, Richang Hong, Tat-Seng Chua:
MMGCN: Multi-modal Graph Convolution Network for Personalized Recommendation of Micro-video. ACM Multimedia 2019: 1437-1445 - [c60]Francesco Gelli, Tiberio Uricchio, Xiangnan He, Alberto Del Bimbo, Tat-Seng Chua:
Learning Subjective Attributes of Images from Auxiliary Sources. ACM Multimedia 2019: 2263-2271 - [c59]Xin Xin, Xiangnan He, Yongfeng Zhang, Yongdong Zhang, Joemon M. Jose:
Relational Collaborative Filtering: Modeling Multiple Item Relations for Recommendation. SIGIR 2019: 125-134 - [c58]Xiang Wang, Xiangnan He, Meng Wang, Fuli Feng, Tat-Seng Chua:
Neural Graph Collaborative Filtering. SIGIR 2019: 165-174 - [c57]Xun Yang, Xiangnan He, Xiang Wang, Yunshan Ma, Fuli Feng, Meng Wang, Tat-Seng Chua:
Interpretable Fashion Matching with Rich Attributes. SIGIR 2019: 775-784 - [c56]Fajie Yuan, Alexandros Karatzoglou, Ioannis Arapakis, Joemon M. Jose, Xiangnan He:
A Simple Convolutional Generative Network for Next Item Recommendation. WSDM 2019: 582-590 - [c55]Jun Xu, Xiangnan He, Hang Li:
Deep Learning for Matching in Search and Recommendation. WSDM 2019: 832-833 - [c54]Yixin Cao, Xiang Wang, Xiangnan He, Zikun Hu, Tat-Seng Chua:
Unifying Knowledge Graph Learning and Recommendation: Towards a Better Understanding of User Preferences. WWW 2019: 151-161 - [c53]Chen Gao, Xiangning Chen, Fuli Feng, Kai Zhao, Xiangnan He, Yong Li, Depeng Jin:
Cross-domain Recommendation Without Sharing User-relevant Data. WWW 2019: 491-502 - [i40]Ming Gao, Xiangnan He, Leihui Chen, Aoying Zhou:
Learning Vertex Representations for Bipartite Networks. CoRR abs/1901.09676 (2019) - [i39]Yixin Cao, Xiang Wang, Xiangnan He, Zikun Hu, Tat-Seng Chua:
Unifying Knowledge Graph Learning and Recommendation: Towards a Better Understanding of User Preferences. CoRR abs/1902.06236 (2019) - [i38]Fuli Feng, Xiangnan He, Jie Tang, Tat-Seng Chua:
Graph Adversarial Training: Dynamically Regularizing Based on Graph Structure. CoRR abs/1902.08226 (2019) - [i37]Xin Xin, Xiangnan He, Yongfeng Zhang, Yongdong Zhang, Joemon M. Jose:
Relational Collaborative Filtering: Modeling Multiple Item Relations for Recommendation. CoRR abs/1904.12796 (2019) - [i36]Wenhui Yu, Xiangnan He, Jian Pei, Xu Chen, Li Xiong, Jinfei Liu, Zheng Qin:
Visually-aware Recommendation with Aesthetic Features. CoRR abs/1905.02009 (2019) - [i35]Xiang Wang, Xiangnan He, Yixin Cao, Meng Liu, Tat-Seng Chua:
KGAT: Knowledge Graph Attention Network for Recommendation. CoRR abs/1905.07854 (2019) - [i34]Xiang Wang, Xiangnan He, Meng Wang, Fuli Feng, Tat-Seng Chua:
Neural Graph Collaborative Filtering. CoRR abs/1905.08108 (2019) - [i33]Yihong Chen, Bei Chen, Xiangnan He, Chen Gao, Yong Li, Jian-Guang Lou, Yue Wang:
LambdaOpt: Learn to Regularize Recommender Models in Finer Levels. CoRR abs/1905.11596 (2019) - [i32]Richang Hong, Daqing Liu, Xiaoyu Mo, Xiangnan He, Hanwang Zhang:
Learning to Compose and Reason with Language Tree Structures for Visual Grounding. CoRR abs/1906.01784 (2019) - [i31]Fajie Yuan, Xiangnan He, Guibing Guo, Zhezhao Xu, Jian Xiong, Xiuqiang He:
Modeling the Past and Future Contexts for Session-based Recommendation. CoRR abs/1906.04473 (2019) - [i30]Xiaoyu Du, Xiangnan He, Fajie Yuan, Jinhui Tang, Zhiguang Qin, Tat-Seng Chua:
Modeling Embedding Dimension Correlations via Convolutional Neural Collaborative Filtering. CoRR abs/1906.11171 (2019) - [i29]Jun Kuang, Yixin Cao, Jianbing Zheng, Xiangnan He, Ming Gao, Aoying Zhou:
Improving Neural Relation Extraction with Implicit Mutual Relations. CoRR abs/1907.05333 (2019) - [i28]Haozhe Wu, Zhiyuan Hu, Jia Jia, Yaohua Bu, Xiangnan He, Tat-Seng Chua:
Mining Unfollow Behavior in Large-Scale Online Social Networks via Spatial-Temporal Interaction. CoRR abs/1911.07156 (2019) - 2018
- [j7]Lizi Liao, Xiangnan He, Hanwang Zhang, Tat-Seng Chua:
Attributed Social Network Embedding. IEEE Trans. Knowl. Data Eng. 30(12): 2257-2270 (2018) - [j6]Xiangnan He, Zhankui He, Jingkuan Song, Zhenguang Liu, Yu-Gang Jiang, Tat-Seng Chua:
NAIS: Neural Attentive Item Similarity Model for Recommendation. IEEE Trans. Knowl. Data Eng. 30(12): 2354-2366 (2018) - [c52]Zan Gao, Deyu Wang, Xiangnan He, Hua Zhang:
Group-Pair Convolutional Neural Networks for Multi-View Based 3D Object Retrieval. AAAI 2018: 2223-2231 - [c51]Wenqiang Lei, Xisen Jin, Min-Yen Kan, Zhaochun Ren, Xiangnan He, Dawei Yin:
Sequicity: Simplifying Task-oriented Dialogue Systems with Single Sequence-to-Sequence Architectures. ACL (1) 2018: 1437-1447 - [c50]Xin Xin, Fajie Yuan, Xiangnan He, Joemon M. Jose:
Batch IS NOT Heavy: Learning Word Representations From All Samples. ACL (1) 2018: 1853-1862 - [c49]Dongxiang Zhang, Long Guo, Xiangnan He, Jie Shao, Sai Wu, Heng Tao Shen:
A Graph-Theoretic Fusion Framework for Unsupervised Entity Resolution. ICDE 2018: 713-724 - [c48]Tiancheng Shen, Jia Jia, Guangyao Shen, Fuli Feng, Xiangnan He, Huanbo Luan, Jie Tang, Thanassis Tiropanis, Tat-Seng Chua, Wendy Hall:
Cross-Domain Depression Detection via Harvesting Social Media. IJCAI 2018: 1611-1617 - [c47]Xiangnan He, Xiaoyu Du, Xiang Wang, Feng Tian, Jinhui Tang, Tat-Seng Chua:
Outer Product-based Neural Collaborative Filtering. IJCAI 2018: 2227-2233 - [c46]Jingtao Ding, Guanghui Yu, Xiangnan He, Yuhan Quan, Yong Li, Tat-Seng Chua, Depeng Jin, Jiajie Yu:
Improving Implicit Recommender Systems with View Data. IJCAI 2018: 3343-3349 - [c45]Han Liu, Xiangnan He, Fuli Feng, Liqiang Nie, Rui Liu, Hanwang Zhang:
Discrete Factorization Machines for Fast Feature-based Recommendation. IJCAI 2018: 3449-3455 - [c44]Zhiyong Cheng, Ying Ding, Xiangnan He, Lei Zhu, Xuemeng Song, Mohan S. Kankanhalli:
A^3NCF: An Adaptive Aspect Attention Model for Rating Prediction. IJCAI 2018: 3748-3754 - [c43]Xiangnan He, Hanwang Zhang, Tat-Seng Chua:
Recommendation Technologies for Multimedia Content. ICMR 2018: 8 - [c42]Francesco Gelli, Tiberio Uricchio, Xiangnan He, Alberto Del Bimbo, Tat-Seng Chua:
Beyond the Product: Discovering Image Posts for Brands in Social Media. ACM Multimedia 2018: 465-473 - [c41]Lizi Liao, Yunshan Ma, Xiangnan He, Richang Hong, Tat-Seng Chua:
Knowledge-aware Multimodal Dialogue Systems. ACM Multimedia 2018: 801-809 - [c40]Lizi Liao, Xiangnan He, Bo Zhao, Chong-Wah Ngo, Tat-Seng Chua:
Interpretable Multimodal Retrieval for Fashion Products. ACM Multimedia 2018: 1571-1579 - [c39]Jingyuan Chen, Xiangnan He, Xuemeng Song, Hanwang Zhang, Liqiang Nie, Tat-Seng Chua:
Venue Prediction for Social Images by Exploiting Rich Temporal Patterns in LBSNs. MMM (2) 2018: 327-339 - [c38]Meng Liu, Xiang Wang, Liqiang Nie, Xiangnan He, Baoquan Chen, Tat-Seng Chua:
Attentive Moment Retrieval in Videos. SIGIR 2018: 15-24 - [c37]Xuemeng Song, Xiang Wang, Liqiang Nie, Xiangnan He, Zhumin Chen, Wei Liu:
A Personal Privacy Preserving Framework: I Let You Know Who Can See What. SIGIR 2018: 295-304 - [c36]Xiangnan He, Zhankui He, Xiaoyu Du, Tat-Seng Chua:
Adversarial Personalized Ranking for Recommendation. SIGIR 2018: 355-364 - [c35]Da Cao, Xiangnan He, Lianhai Miao, Yahui An, Chao Yang, Richang Hong:
Attentive Group Recommendation. SIGIR 2018: 645-654 - [c34]Ming Gao, Leihui Chen, Xiangnan He, Aoying Zhou:
BiNE: Bipartite Network Embedding. SIGIR 2018: 715-724 - [c33]Xin Luo, Liqiang Nie, Xiangnan He, Ye Wu, Zhen-Duo Chen, Xin-Shun Xu:
Fast Scalable Supervised Hashing. SIGIR 2018: 735-744 - [c32]Jun Xu, Xiangnan He, Hang Li:
Deep Learning for Matching in Search and Recommendation. SIGIR 2018: 1365-1368 - [c31]Zhaochun Ren, Xiangnan He, Dawei Yin, Maarten de Rijke:
Information Discovery in E-commerce: Half-day SIGIR 2018 Tutorial. SIGIR 2018: 1379-1382 - [c30]Fajie Yuan, Xin Xin, Xiangnan He, Guibing Guo, Weinan Zhang, Tat-Seng Chua, Joemon M. Jose:
fBGD: Learning Embeddings From Positive Unlabeled Data with BGD. UAI 2018: 198-207 - [c29]Jingtao Ding, Fuli Feng, Xiangnan He, Guanghui Yu, Yong Li, Depeng Jin:
An Improved Sampler for Bayesian Personalized Ranking by Leveraging View Data. WWW (Companion Volume) 2018: 13-14 - [c28]Wenhui Yu, Huidi Zhang, Xiangnan He, Xu Chen, Li Xiong, Zheng Qin:
Aesthetic-based Clothing Recommendation. WWW 2018: 649-658 - [c27]Fuli Feng, Xiangnan He, Yiqun Liu, Liqiang Nie, Tat-Seng Chua:
Learning on Partial-Order Hypergraphs. WWW 2018: 1523-1532 - [c26]Xiang Wang, Xiangnan He, Fuli Feng, Liqiang Nie, Tat-Seng Chua:
TEM: Tree-enhanced Embedding Model for Explainable Recommendation. WWW 2018: 1543-1552 - [i27]Han Liu, Xiangnan He, Fuli Feng, Liqiang Nie, Rui Liu, Hanwang Zhang:
Discrete Factorization Machines for Fast Feature-based Recommendation. CoRR abs/1805.02232 (2018) - [i26]Xiangnan He, Zhankui He, Xiaoyu Du, Tat-Seng Chua:
Adversarial Personalized Ranking for Recommendation. CoRR abs/1808.03908 (2018) - [i25]Xiangnan He, Xiaoyu Du, Xiang Wang, Feng Tian, Jinhui Tang, Tat-Seng Chua:
Outer Product-based Neural Collaborative Filtering. CoRR abs/1808.03912 (2018) - [i24]Fajie Yuan, Alexandros Karatzoglou, Ioannis Arapakis, Joemon M. Jose, Xiangnan He:
A Simple but Hard-to-Beat Baseline for Session-based Recommendations. CoRR abs/1808.05163 (2018) - [i23]Wenhui Yu, Huidi Zhang, Xiangnan He, Xu Chen, Li Xiong, Zheng Qin:
Aesthetic-based Clothing Recommendation. CoRR abs/1809.05822 (2018) - [i22]Xiangnan He, Zhankui He, Jingkuan Song, Zhenguang Liu, Yu-Gang Jiang, Tat-Seng Chua:
NAIS: Neural Attentive Item Similarity Model for Recommendation. CoRR abs/1809.07053 (2018) - [i21]Jinhui Tang, Xiangnan He, Xiaoyu Du, Fajie Yuan, Qi Tian, Tat-Seng Chua:
Adversarial Training Towards Robust Multimedia Recommender System. CoRR abs/1809.07062 (2018) - [i20]Chen Gao, Xiangnan He, Dahua Gan, Xiangning Chen, Fuli Feng, Yong Li, Tat-Seng Chua, Depeng Jin:
Learning Recommender Systems from Multi-Behavior Data. CoRR abs/1809.08161 (2018) - [i19]Jingtao Ding, Guanghui Yu, Xiangnan He, Yong Li, Depeng Jin:
Sampler Design for Bayesian Personalized Ranking by Leveraging View Data. CoRR abs/1809.08162 (2018) - [i18]Fuli Feng, Xiangnan He, Xiang Wang, Cheng Luo, Yiqun Liu, Tat-Seng Chua:
Temporal Relational Ranking for Stock Prediction. CoRR abs/1809.09441 (2018) - [i17]Yezheng Liu, Zhe Li, Chong Zhou, Yuanchun Jiang, Jianshan Sun, Meng Wang, Xiangnan He:
Generative Adversarial Active Learning for Unsupervised Outlier Detection. CoRR abs/1809.10816 (2018) - [i16]Xiaoyan Gao, Fuli Feng, Xiangnan He, Heyan Huang, Xinyu Guan, Chong Feng, Zhaoyan Ming, Tat-Seng Chua:
Visually-aware Collaborative Food Recommendation. CoRR abs/1810.05032 (2018) - [i15]Fuli Feng, Huimin Chen, Xiangnan He, Ji Ding, Maosong Sun, Tat-Seng Chua:
Improving Stock Movement Prediction with Adversarial Training. CoRR abs/1810.09936 (2018) - [i14]Xinyu Guan, Zhiyong Cheng, Xiangnan He, Yongfeng Zhang, Zhibo Zhu, Qinke Peng, Tat-Seng Chua:
Attentive Aspect Modeling for Review-aware Recommendation. CoRR abs/1811.04375 (2018) - [i13]Feng Xue, Xiangnan He, Xiang Wang, Jiandong Xu, Kai Liu, Richang Hong:
Deep Item-based Collaborative Filtering for Top-N Recommendation. CoRR abs/1811.04392 (2018) - [i12]Xiangnan He, Jinhui Tang, Xiaoyu Du, Richang Hong, Tongwei Ren, Tat-Seng Chua:
Fast Matrix Factorization with Non-Uniform Weights on Missing Data. CoRR abs/1811.04411 (2018) - [i11]Xiang Wang, Dingxian Wang, Canran Xu, Xiangnan He, Yixin Cao, Tat-Seng Chua:
Explainable Reasoning over Knowledge Graphs for Recommendation. CoRR abs/1811.04540 (2018) - [i10]Long Chen, Hanwang Zhang, Jun Xiao, Xiangnan He, Shiliang Pu, Shih-Fu Chang:
Scene Dynamics: Counterfactual Critic Multi-Agent Training for Scene Graph Generation. CoRR abs/1812.02347 (2018) - 2017
- [j5]Da Cao, Liqiang Nie, Xiangnan He, Xiaochi Wei, Jialie Shen, Shunxiang Wu, Tat-Seng Chua:
Version-sensitive mobile App recommendation. Inf. Sci. 381: 161-175 (2017) - [j4]Xiao Chen, Min Liu, Yaqin Zhou, Zhongcheng Li, Shuang Chen, Xiangnan He:
A Truthful Incentive Mechanism for Online Recruitment in Mobile Crowd Sensing System. Sensors 17(1): 79 (2017) - [j3]Xiangnan He, Ming Gao, Min-Yen Kan, Dingxian Wang:
BiRank: Towards Ranking on Bipartite Graphs. IEEE Trans. Knowl. Data Eng. 29(1): 57-71 (2017) - [j2]Lei Zhu, Zi Huang, Xiaobai Liu, Xiangnan He, Jiande Sun, Xiaofang Zhou:
Discrete Multimodal Hashing With Canonical Views for Robust Mobile Landmark Search. IEEE Trans. Multim. 19(9): 2066-2079 (2017) - [j1]Da Cao, Xiangnan He, Liqiang Nie, Xiaochi Wei, Xia Hu, Shunxiang Wu, Tat-Seng Chua:
Cross-Platform App Recommendation by Jointly Modeling Ratings and Texts. ACM Trans. Inf. Syst. 35(4): 37:1-37:27 (2017) - [c25]Manjira Sinha, Xiangnan He, Alessandro Bozzon, Sandya Mannarswamy, Pradeep K. Murukannaiah, Tridib Mukherjee:
SMASC 2017: First International Workshop on Social Media Analytics for Smart Cities. CIKM 2017: 2567-2568 - [c24]Lizi Liao, Xiangnan He, Zhaochun Ren, Liqiang Nie, Huan Xu, Tat-Seng Chua:
Representativeness-aware Aspect Analysis for Brand Monitoring in Social Media. IJCAI 2017: 310-316 - [c23]Jun Xiao, Hao Ye, Xiangnan He, Hanwang Zhang, Fei Wu, Tat-Seng Chua:
Attentional Factorization Machines: Learning the Weight of Feature Interactions via Attention Networks. IJCAI 2017: 3119-3125 - [c22]Wenqiang Lei, Xuancong Wang, Meichun Liu, Ilija Ilievski, Xiangnan He, Min-Yen Kan:
SWIM: A Simple Word Interaction Model for Implicit Discourse Relation Recognition. IJCAI 2017: 4026-4032 - [c21]Zhenguang Liu, Li Cheng, Anan Liu, Luming Zhang, Xiangnan He, Roger Zimmermann:
Multiview and Multimodal Pervasive Indoor Localization. ACM Multimedia 2017: 109-117 - [c20]Liqiang Nie, Xiang Wang, Jianglong Zhang, Xiangnan He, Hanwang Zhang, Richang Hong, Qi Tian:
Enhancing Micro-video Understanding by Harnessing External Sounds. ACM Multimedia 2017: 1192-1200 - [c19]Dejing Xu, Zhou Zhao, Jun Xiao, Fei Wu, Hanwang Zhang, Xiangnan He, Yueting Zhuang:
Video Question Answering via Gradually Refined Attention over Appearance and Motion. ACM Multimedia 2017: 1645-1653 - [c18]Francesco Gelli, Xiangnan He, Tao Chen, Tat-Seng Chua:
How Personality Affects our Likes: Towards a Better Understanding of Actionable Images. ACM Multimedia 2017: 1828-1837 - [c17]Xiang Wang, Xiangnan He, Liqiang Nie, Tat-Seng Chua:
Item Silk Road: Recommending Items from Information Domains to Social Users. SIGIR 2017: 185-194 - [c16]Jingyuan Chen, Hanwang Zhang, Xiangnan He, Liqiang Nie, Wei Liu, Tat-Seng Chua:
Attentive Collaborative Filtering: Multimedia Recommendation with Item- and Component-Level Attention. SIGIR 2017: 335-344 - [c15]Xiangnan He, Tat-Seng Chua:
Neural Factorization Machines for Sparse Predictive Analytics. SIGIR 2017: 355-364 - [c14]Da Cao, Liqiang Nie, Xiangnan He, Xiaochi Wei, Shunzhi Zhu, Tat-Seng Chua:
Embedding Factorization Models for Jointly Recommending Items and User Generated Lists. SIGIR 2017: 585-594 - [c13]Xiangnan He, Lizi Liao, Hanwang Zhang, Liqiang Nie, Xia Hu, Tat-Seng Chua:
Neural Collaborative Filtering. WWW 2017: 173-182 - [c12]Immanuel Bayer, Xiangnan He, Bhargav Kanagal, Steffen Rendle:
A Generic Coordinate Descent Framework for Learning from Implicit Feedback. WWW 2017: 1341-1350 - [i9]Lizi Liao, Xiangnan He, Hanwang Zhang, Tat-Seng Chua:
Attributed Social Network Embedding. CoRR abs/1705.04969 (2017) - [i8]Xiang Wang, Xiangnan He, Liqiang Nie, Tat-Seng Chua:
Item Silk Road: Recommending Items from Information Domains to Social Users. CoRR abs/1706.03205 (2017) - [i7]Lei Zhu, Zi Huang, Xiaobai Liu, Xiangnan He, Jingkuan Song, Xiaofang Zhou:
Discrete Multi-modal Hashing with Canonical Views for Robust Mobile Landmark Search. CoRR abs/1707.04047 (2017) - [i6]Xiangnan He, Ming Gao, Min-Yen Kan, Dingxian Wang:
BiRank: Towards Ranking on Bipartite Graphs. CoRR abs/1708.04396 (2017) - [i5]Jun Xiao, Hao Ye, Xiangnan He, Hanwang Zhang, Fei Wu, Tat-Seng Chua:
Attentional Factorization Machines: Learning the Weight of Feature Interactions via Attention Networks. CoRR abs/1708.04617 (2017) - [i4]Xiangnan He, Hanwang Zhang, Min-Yen Kan, Tat-Seng Chua:
Fast Matrix Factorization for Online Recommendation with Implicit Feedback. CoRR abs/1708.05024 (2017) - [i3]Xiangnan He, Tat-Seng Chua:
Neural Factorization Machines for Sparse Predictive Analytics. CoRR abs/1708.05027 (2017) - [i2]Xiangnan He, Lizi Liao, Hanwang Zhang, Liqiang Nie, Xia Hu, Tat-Seng Chua:
Neural Collaborative Filtering. CoRR abs/1708.05031 (2017) - 2016
- [c11]Tao Chen, Xiangnan He, Min-Yen Kan:
Context-aware Image Tweet Modelling and Recommendation. ACM Multimedia 2016: 1018-1027 - [c10]Jianglong Zhang, Liqiang Nie, Xiang Wang, Xiangnan He, Xianglin Huang, Tat-Seng Chua:
Shorter-is-Better: Venue Category Estimation from Micro-Video. ACM Multimedia 2016: 1415-1424 - [c9]Hanwang Zhang, Fumin Shen, Wei Liu, Xiangnan He, Huanbo Luan, Tat-Seng Chua:
Discrete Collaborative Filtering. SIGIR 2016: 325-334 - [c8]Xiangnan He, Hanwang Zhang, Min-Yen Kan, Tat-Seng Chua:
Fast Matrix Factorization for Online Recommendation with Implicit Feedback. SIGIR 2016: 549-558 - [i1]Immanuel Bayer, Xiangnan He, Bhargav Kanagal, Steffen Rendle:
A Generic Coordinate Descent Framework for Learning from Implicit Feedback. CoRR abs/1611.04666 (2016) - 2015
- [c7]Tao Chen, Hany M. SalahEldeen, Xiangnan He, Min-Yen Kan, Dongyuan Lu:
VELDA: Relating an Image Tweet's Text and Images. AAAI 2015: 30-36 - [c6]Xiangnan He, Tao Chen, Min-Yen Kan, Xiao Chen:
TriRank: Review-aware Explainable Recommendation by Modeling Aspects. CIKM 2015: 1661-1670 - [c5]Xiao Chen, Min Liu, Yaqin Zhou, Zhongcheng Li, Shuang Chen, Xiangnan He:
Differential spread strategy: An incentive for advertisement dissemination. ISCC 2015: 595-601 - 2014
- [c4]Xiangnan He, Ming Gao, Min-Yen Kan, Yiqun Liu, Kazunari Sugiyama:
Predicting the popularity of web 2.0 items based on user comments. SIGIR 2014: 233-242 - [c3]Xiangnan He, Min-Yen Kan, Peichu Xie, Xiao Chen:
Comment-based multi-view clustering of web 2.0 items. WWW 2014: 771-782 - 2013
- [c2]Yiping Jin, Min-Yen Kan, Jun-Ping Ng, Xiangnan He:
Mining Scientific Terms and their Definitions: A Study of the ACL Anthology. EMNLP 2013: 780-790 - 2010
- [c1]Ming Gao, Xiangnan He, Cheqing Jin, Xiaoling Wang, Aoying Zhou:
Recording How-Provenance on Probabilistic Databases. APWeb 2010: 205-211
Coauthor Index
[j82] [j77] [c200] [i184] [i171] [i160] [j71] [j67] [c161] [c156] [i147] [i146] [j40] [j39] [j38] [c150] [c149] [c144] [c143] [c141] [c127] [i120] [i119] [i117] [i112] [i107] [i106] [j37] [j32] [j31] [j30] [j28] [c126] [c123] [c120] [c117] [c112] [c111] [c110] [c109] [i95] [i94] [i87] [i86] [i78] [i77] [i76] [i75] [j24] [j23] [j19] [j17] [j15] [j13] [c106] [c97] [c96] [c95] [c94] [c92] [c88] [c83] [c82] [c81] [c80] [c78] [i69] [i68] [i65] [i61] [i60] [i57] [i54] [i53] [i49] [i45] [j11] [j10] [j8] [c76] [c74] [c72] [c67] [c66] [c62] [c61] [c60] [c58] [c57] [c54] [i39] [i38] [i35] [i34] [i30] [i28] [j7] [j6] [c48] [c47] [c46] [c43] [c42] [c41] [c40] [c39] [c38] [c36] [c30] [c27] [c26] [i26] [i25] [i22] [i21] [i20] [i18] [i16] [i15] [i14] [i12] [i11] [j5] [j1] [c24] [c23] [c18] [c17] [c16] [c15] [c14] [c13] [i9] [i8] [i5] [i4] [i3] [i2] [c10] [c9] [c8]
[j81] [j79] [j78] [j77] [j75] [c203] [c202] [c199] [c198] [c197] [c196] [c195] [c193] [c191] [c190] [c188] [c187] [c186] [i181] [i180] [i179] [i178] [i177] [i176] [i174] [i172] [i171] [i170] [i169] [i168] [i165] [i162] [i156] [i155] [j74] [j73] [j71] [j70] [j67] [j63] [j57] [j56] [c184] [c182] [c180] [c179] [c176] [c172] [c171] [c167] [c166] [c165] [c164] [c161] [c160] [c159] [c158] [c155] [c153] [i154] [i147] [i146] [i145] [i144] [i143] [i140] [i138] [i137] [i136] [i134] [i133] [i128] [i126] [i122] [j45] [c150] [c142] [c140] [c135] [c130] [c129] [c127] [i120] [i112] [i110] [i109] [i103] [i101] [i100] [i96] [j36] [j34] [j32] [j31] [c126] [c123] [c120] [c119] [c116] [c115] [c113] [c112] [c111] [c110] [c107] [i93] [i90] [i88] [i87] [i86] [i82] [i76] [j17] [c100] [c99] [c96] [c85] [c82] [i71] [i68] [i59] [i57] [i55] [i50] [i49] [i47] [i46] [i45] [i44] [i43] [j11] [c72] [c67] [c58] [c57] [c53] [i38] [i34] [c48] [c45] [c29] [c27] [c26] [i27] [i20] [i18] [i16] [i15]
[j75] [c203] [c195] [c191] [c190] [c188] [c187] [c186] [i181] [i180] [i178] [i177] [i176] [i172] [i171] [i168] [i162] [i156] [i155] [c184] [c182] [c180] [c167] [c165] [c164] [c161] [c160] [c159] [c158] [i147] [i146] [i145] [i144] [i143] [i136] [i133] [i128] [c135] [c130] [c127] [i101] [c120] [c111] [c110] [i87] [i76] [i57] [i49]
[j82] [j80] [j77] [c205] [c201] [c200] [c194] [c192] [c189] [i184] [i183] [i182] [i173] [i167] [i166] [i164] [i163] [i160] [i158] [i157] [j72] [j71] [j62] [j58] [j56] [c173] [c170] [c169] [c168] [c162] [c157] [c156] [c154] [c151] [i148] [i131] [i129] [i127] [i125] [i124] [j51] [j50] [j40] [c147] [c144] [c143] [c141] [c134] [c132] [c128] [i121] [i120] [i119] [i114] [i112] [i111] [i107] [i106] [i97] [c123] [c120] [c117] [c116] [c113] [c109] [i94] [i92] [i87] [i84] [i83] [i78] [i77] [i75] [j24] [j23] [c99] [c97] [c95] [c94] [c92] [c90] [c88] [c80] [c78] [i73] [i72] [i71] [i65] [i61] [i54] [i53] [i47] [i46] [j11] [j9] [c76] [c74] [c66] [c61] [c58] [c57] [c54] [i39] [i35] [i34] [c47] [c38] [c37] [c26] [i25] [i18] [i13] [i11] [c20] [c17] [i8] [c10]
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last updated on 2024-12-10 21:40 CET by the dblp team
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