Popularity-aware Distributionally Robust Optimization for Recommendation System
Author:
Affiliation:
1. National University of Singapore, Singapore, Singapore
2. University of Science and Technology of China, Hefei, China, China
Funder
Huawei International Pte Ltd.
Publisher
ACM
Link
https://dl.acm.org/doi/pdf/10.1145/3583780.3615492
Reference54 articles.
1. Keqin Bao Jizhi Zhang Yang Zhang Wenjie Wang Fuli Feng and Xiangnan He. 2023. TALLRec: An Effective and Efficient Tuning Framework to Align Large Language Model with Recommendation. In RecSys. ACM. Keqin Bao Jizhi Zhang Yang Zhang Wenjie Wang Fuli Feng and Xiangnan He. 2023. TALLRec: An Effective and Efficient Tuning Framework to Align Large Language Model with Recommendation. In RecSys. ACM.
2. Roc'io Ca namares and Pablo Castells. 2017. A probabilistic reformulation of memory-based collaborative filtering: Implications on popularity biases. In SIGIR. ACM 215--224. Roc'io Ca namares and Pablo Castells. 2017. A probabilistic reformulation of memory-based collaborative filtering: Implications on popularity biases. In SIGIR. ACM 215--224.
3. John Duchi and Hongseok Namkoong. 2018. Learning models with uniform performance via distributionally robust optimization. arXiv:1810.08750. John Duchi and Hongseok Namkoong. 2018. Learning models with uniform performance via distributionally robust optimization. arXiv:1810.08750.
4. A fusion collaborative filtering method for sparse data in recommender systems
5. Zuohui Fu , Yikun Xian , Shijie Geng , Gerard De Melo, and Yongfeng Zhang . 2021 . Popcorn : Human-in-the-loop Popularity Debiasing in Conversational Recommender Systems. In CIKM. ACM , 494--503. Zuohui Fu, Yikun Xian, Shijie Geng, Gerard De Melo, and Yongfeng Zhang. 2021. Popcorn: Human-in-the-loop Popularity Debiasing in Conversational Recommender Systems. In CIKM. ACM, 494--503.
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