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RecSys 2024: Bari, Italy - Challenge
- Proceedings of the Recommender Systems Challenge 2024, RecSysChallenge 2024, Bari, Italy, October 14-18, 2024. ACM 2024, ISBN 979-8-4007-1127-5
- Johannes Kruse
, Kasper Lindskow
, Saikishore Kalloori
, Marco Polignano
, Claudio Pomo
, Abhishek Srivastava
, Anshuk Uppal
, Michael Riis Andersen
, Jes Frellsen
:
EB-NeRD a large-scale dataset for news recommendation. 1-11 - Antonio Ferrara
, Marco Valentini
, Paolo Masciullo
, Antonio De Candia
, Davide Abbattista
, Riccardo Fusco
, Claudio Pomo
, Vito Walter Anelli
, Giovanni Maria Biancofiore
, Ludovico Boratto
, Fedelucio Narducci
:
DIVAN: Deep-Interest Virality-Aware Network to Exploit Temporal Dynamics in News Recommendation. 12-16 - Andrea Alari
, Lorenzo Campana
, Federico Giuseppe Ciliberto
, Saverio Maggese
, Carlo Sgaravatti
, Francesco Zanella
, Andrea Pisani
, Maurizio Ferrari Dacrema
:
Exploiting Contextual Normalizations and Article Endorsement for News Recommendation. 17-21 - Lucien Heitz
, Sanne Vrijenhoek
, Oana Inel
:
Recommendations for the Recommenders: Reflections on Prioritizing Diversity in the RecSys Challenge. 22-26 - Tetsuro Sugiura
, Yosuke Yamagishi
, Yodai Kishimoto
:
Leveraging LightGBM Ranker for Efficient Large-Scale News Recommendation Systems. 27-31 - Qi Zhang
, Jieming Zhu
, Jiansheng Sun
, Guohao Cai
, Ruining Yu
, Bangzheng He
, Liangbi Li
:
Enhancing News Recommendation with Real-Time Feedback and Generative Sequence Modeling. 32-36 - Tomomu Iwai
, Akihiro Tomita
, Tomoyuki Arai
, Hiroki Ogawa
, Takuma Saito
:
Harnessing Temporal Dynamics and Content: An Ensemble of Gradient Boosting Machines for News Recommendation. 37-41 - Kazuki Fujikawa
, Naoki Murakami
, Yuki Sugawara
:
Enhancing News Recommendation with Transformers and Ensemble Learning. 42-47 - Juan Manuel Rodriguez
, Antonela Tommasel
:
Leveraging User History with Transformers for News Clicking: The DArgk Approach. 48-52 - Taofeng Xue
, Zhimin Lin
, Zijian Zhang
, Linsen Guo
, Haoru Chen
, Mengjiao Bao
, Peng Yan
:
Large Scale Hierarchical User Interest Modeling for Click-through Rate Prediction. 53-57
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