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RecSys 2024: Bari, Italy - Challenge

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Proceedings of the Recommender Systems Challenge 2024, RecSysChallenge 2024, Bari, Italy, October 14-18, 2024. ACM 2024, ISBN 979-8-4007-1127-5
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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
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  - conf/recsys/FerraraVMCAFPAB24
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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
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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
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  - conf/recsys/HeitzVI24
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Lucien Heitz, Sanne Vrijenhoek, Oana Inel:
Recommendations for the Recommenders: Reflections on Prioritizing Diversity in the RecSys Challenge. 22-26
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Tetsuro Sugiura, Yosuke Yamagishi, Yodai Kishimoto:
Leveraging LightGBM Ranker for Efficient Large-Scale News Recommendation Systems. 27-31
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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
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  - conf/recsys/IwaiTAOS24
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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
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Kazuki Fujikawa, Naoki Murakami, Yuki Sugawara:
Enhancing News Recommendation with Transformers and Ensemble Learning. 42-47
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Juan Manuel Rodriguez, Antonela Tommasel:
Leveraging User History with Transformers for News Clicking: The DArgk Approach. 48-52
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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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