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ORCIDRecSys 2023: Singapore - Challenge
- ACM RecSys Challenge 2023, Singapore, 19 September 2023. ACM 2023
- Rahul Agrawal
, Sarang Brahme, Sourav Maitra, Abhishek Srivastava, Athirai A. Irissappane, Yong Liu, Saikishore Kalloori:
RecSys Challenge 2023 Dataset: Ads Recommendations in Online Advertising. 1-3 - Jiawei Jiang, Bing Wang, Jingyuan Wang:
Integrating Explicit and Implicit Feature Interactions for Online Ad Installation Forecasting. 4-8 - Hyeonwoo Kim, Wonsung Lee:
Lightweight Boosting Models for User Response Prediction Using Adversarial Validation. 9-13 - Xingmei Wang, Yankai Wang, Defu Lian:
A Simple and Robust Ensemble For Click-Through Rate Prediction. 14-17 - Qi Zhang, Zhibin Zhang, Biao Lu, Bangzheng He, Liangbi Li, Zhenhua Dong:
Large Scale CVR Prediction through Hierarchical History Modeling. 18-22 - Yichao Lu, Maksims Volkovs:
Robust User Engagement Modeling With Transformers and Self Supervision. 23-27 - Taehee Kim, Seungyun Baek, Taehyeon Jeon, Hojin Jung, Joonhong Kim, Taeho Lee:
Capturing Performance and Privacy by Assembling Avengers of Online Advertising. 28-32 - Paolo Basso, Arturo Benedetti, Nicola Cecere, Alessandro Maranelli, Salvatore Marragony, Samuele Peri, Andrea Riboni, Alessandro Verosimile, Davide Zanutto, Maurizio Ferrari Dacrema:
Pessimistic Rescaling and Distribution Shift of Boosting Models for Impression-Aware Online Advertising Recommendation. 33-38 - Xiaoteng Shen, Liangcai Su, Zhutian Lin, Xi Xiao:
A Simple yet Strong Approach for Installation Prediction in ShareChat Ads. 39-43 - Chendi Xue, Xinyao Wang, Yu Zhou, Poovaiah M. Palangappa, Rita Brugarolas Brufau, Aasavari Dhananjay Kakne, Ravi Motwani, Ke Ding, Jian Zhang:
Graph Enhanced Feature Engineering for Privacy Preserving Recommendation Systems. 44-51
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