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Popularity-aware Distributionally Robust Optimization for Recommendation System

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Tat-Seng ChuaAuthors Info & Claims

CIKM '23: Proceedings of the 32nd ACM International Conference on Information and Knowledge Management

Pages 4967 - 4973

https://doi.org/10.1145/3583780.3615492

Published: 21 October 2023 Publication History

Abstract

Collaborative Filtering (CF) has been widely applied for personalized recommendations in various industrial applications. However, due to the training strategy of Empirical Risk Minimization, CF models tend to favor popular items, resulting in inferior performance on sparse users and items. To enhance the CF representation learning of sparse users and items without sacrificing the performance of popular items, we propose a novel Popularity- aware Distributionally Robust Optimization (PDRO) framework. In particular, PDRO emphasizes the optimization of sparse users/items, while incorporating item popularity to preserve the performance of popular items through two modules. First, an implicit module develops a new popularity-aware DRO objective, paying more attention to items that will potentially become popular over time. Second, an explicit module that directly predicts the popularity of items to help the estimation of user-item matching scores. We apply PDRO to a micro-video recommendation scenario and implement it on two representative backend models. Extensive experiments on a real-world industrial dataset, as well as two public benchmark datasets, validate the efficacy of our proposed PDRO. Additionally, we perform an offline A/B test on the industrial dataset, further demonstrating the superiority of PDRO in real-world application scenarios.

References

[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.

[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.

[3]

John Duchi and Hongseok Namkoong. 2018. Learning models with uniform performance via distributionally robust optimization. arXiv:1810.08750.

[4]

Chenjiao Feng, Jiye Liang, Peng Song, and Zhiqiang Wang. 2020. A fusion collaborative filtering method for sparse data in recommender systems. Inf. Sci., Vol. 521 (2020), 365--379.

Digital Library

[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.

[6]

Chongming Gao, Shijun Li, Yuan Zhang, Jiawei Chen, Biao Li, Wenqiang Lei, Peng Jiang, and Xiangnan He. 2022a. KuaiRand: An Unbiased Sequential Recommendation Dataset with Randomly Exposed Videos. In CIKM. ACM, 3953--3957.

[7]

Chen Gao, Yu Zheng, Wenjie Wang, Fuli Feng, Xiangnan He, and Yong Li. 2022b. Causal inference in recommender systems: A survey and future directions. arXiv:2208.12397.

[8]

Alexandre Gilotte, Clé ment Calauzè nes, Thomas Nedelec, Alexandre Abraham, and Simon Dollé. 2018. Offline A/B Testing for Recommender Systems. In WSDM. ACM, 198--206.

[9]

Siyuan Guo, Lixin Zou, Yiding Liu, Wenwen Ye, Suqi Cheng, Shuaiqiang Wang, Hechang Chen, Dawei Yin, and Yi Chang. 2021. Enhanced Doubly Robust Learning for Debiasing Post-Click Conversion Rate Estimation. In SIGIR. ACM, 275--284.

[10]

Tatsunori Hashimoto, Megha Srivastava, Hongseok Namkoong, and Percy Liang. 2018. Fairness without demographics in repeated loss minimization. In ICML. PMLR, 1929--1938.

[11]

Xiangnan He, Kuan Deng, Xiang Wang, Yan Li, Yongdong Zhang, and Meng Wang. 2020. Lightgcn: Simplifying and powering graph convolution network for recommendation. In SIGIR. 639--648.

Digital Library

[12]

Xiangnan He, Lizi Liao, Hanwang Zhang, Liqiang Nie, Xia Hu, and Tat-Seng Chua. 2017. Neural collaborative filtering. In WWW. ACM, 173--182.

[13]

Weihua Hu, Gang Niu, Issei Sato, and Masashi Sugiyama. 2018. Does distributionally robust supervised learning give robust classifiers?. In ICML. PMLR, 2029--2037.

[14]

Jin Huang, Harrie Oosterhuis, Maarten De Rijke, and Herke Van Hoof. 2020. Keeping dataset biases out of the simulation: A debiased simulator for reinforcement learning based recommender systems. In RecSys. ACM, 190--199.

[15]

Thorsten Joachims, Adith Swaminathan, and Tobias Schnabel. 2017. Unbiased Learning-to-Rank with Biased Feedback. In WSDM, Maarten de Rijke, Milad Shokouhi, Andrew Tomkins, and Min Zhang (Eds.). ACM, 781--789.

[16]

Yehuda Koren, Robert Bell, and Chris Volinsky. 2009. Matrix factorization techniques for recommender systems. Computer, Vol. 42, 8 (2009), 30--37.

Digital Library

[17]

Qian Li, Xiangmeng Wang, Zhichao Wang, and Guandong Xu. 2023. Be causal: De-biasing social network confounding in recommendation. KDD, 1--23.

[18]

Dawen Liang, Laurent Charlin, James McInerney, and David M Blei. 2016. Modeling user exposure in recommendation. In WWW. ACM, 951--961.

[19]

Dawen Liang, Rahul G Krishnan, Matthew D Hoffman, and Tony Jebara. 2018. Variational Autoencoders for Collaborative Filtering. In WWW. ACM, 689--698.

[20]

Dugang Liu, Pengxiang Cheng, Hong Zhu, Zhenhua Dong, Xiuqiang He, Weike Pan, and Zhong Ming. 2021. Mitigating confounding bias in recommendation via information bottleneck. In RecSys. ACM, 351--360.

[21]

Jiashuo Liu, Jiayun Wu, Bo Li, and Peng Cui. 2022. Distributionally robust optimization with data geometry. In NeurIPS. Curran Associates, Inc., 33689--33701.

[22]

Zhongzhou Liu, Yuan Fang, and Min Wu. 2023. Mitigating popularity bias for users and items with fairness-centric adaptive recommendation. ACM Transactions on Information Systems, Vol. 41, 3 (2023), 1--27.

Digital Library

[23]

Jianxin Ma, Chang Zhou, Peng Cui, Hongxia Yang, and Wenwu Zhu. 2019. Learning Disentangled Representations for Recommendation. In NeurIPS. Curran Associates, Inc., 5712--5723.

[24]

Masoud Mansoury, Himan Abdollahpouri, Mykola Pechenizkiy, Bamshad Mobasher, and Robin Burke. 2021. A graph-based approach for mitigating multi-sided exposure bias in recommender systems. TOIS, Vol. 40, 2 (2021), 1--31.

Digital Library

[25]

Paul Michel, Tatsunori Hashimoto, and Graham Neubig. 2021. Modeling the second player in distributionally robust optimization. In ICLR.

[26]

Paul Michel, Tatsunori Hashimoto, and Graham Neubig. 2022. Distributionally robust models with parametric likelihood ratios. In ICLR.

[27]

Preksha Nema, Alexandros Karatzoglou, and Filip Radlinski. 2021. Disentangling Preference Representations for Recommendation Critiquing with ß-VAE. In CIKM. ACM, 1356--1365.

[28]

Liqiang Nie, Leigang Qu, Dai Meng, Min Zhang, Qi Tian, and Alberto Del Bimbo. 2022. Search-oriented micro-video captioning. In MM. ACM, 3234--3243.

[29]

Yonatan Oren, Shiori Sagawa, Tatsunori B Hashimoto, and Percy Liang. 2019. Distributionally robust language modeling. arXiv:1909.02060.

[30]

Eli Pariser. 2011. The filter bubble: What the Internet is hiding from you. Penguin UK.

Digital Library

[31]

Vihari Piratla, Praneeth Netrapalli, and Sunita Sarawagi. 2022. Focus on the common good: group distributional robustness follows. In ICLR.

[32]

Leigang Qu, Meng Liu, Jianlong Wu, Zan Gao, and Liqiang Nie. 2021. Dynamic modality interaction modeling for image-text retrieval. In SIGIR. ACM, 1104--1113.

[33]

Hamed Rahimian and Sanjay Mehrotra. 2019. Distributionally robust optimization: A review. arXiv:1908.05659.

[34]

Shiori Sagawa, Pang Wei Koh, Tatsunori B Hashimoto, and Percy Liang. 2020. Distributionally robust neural networks for group shifts: on the importance of regularization for worst-case generalization. In ICLR.

[35]

Tobias Schnabel, Adith Swaminathan, Ashudeep Singh, Navin Chandak, and Thorsten Joachims. 2016. Recommendations as Treatments: Debiasing Learning and Evaluation. In ICML, Vol. 48. JMLR.org, 1670--1679.

[36]

Harald Steck. 2011. Item popularity and recommendation accuracy. In RecSys. ACM, 125--132.

[37]

Wenjie Wang, Fuli Feng, Liqiang Nie, and Tat-Seng Chua. 2022a. User-controllable recommendation against filter bubbles. In SIGIR. ACM, 1251--1261.

[38]

Wenjie Wang, Xinyu Lin, Fuli Feng, Xiangnan He, Min Lin, and Tat-Seng Chua. 2022b. Causal Representation Learning for Out-of-Distribution Recommendation. In WWW. ACM, 3562--3571.

[39]

Yixin Wang, Dawen Liang, Laurent Charlin, and David M Blei. 2020. Causal inference for recommender systems. In RecSys. ACM, 426--431.

[40]

Tianxin Wei, Fuli Feng, Jiawei Chen, Ziwei Wu, Jinfeng Yi, and Xiangnan He. 2021. Model-agnostic counterfactual reasoning for eliminating popularity bias in recommender system. In KDD. ACM, 1791--1800.

[41]

Hongyi Wen, Xinyang Yi, Tiansheng Yao, Jiaxi Tang, Lichan Hong, and Ed H Chi. 2022. Distributionally-robust recommendations for improving worst-case user experience. In WWW. ACM, 3606--3610.

[42]

Zhe Xie, Chengxuan Liu, Yichi Zhang, Hongtao Lu, Dong Wang, and Yue Ding. 2021. Adversarial and contrastive variational autoencoder for sequential recommendation. In WWW. ACM, 449--459.

[43]

Guipeng Xv, Chen Lin, Hui Li, Jinsong Su, Weiyao Ye, and Yewang Chen. 2022. Neutralizing Popularity Bias in Recommendation Models. In SIGIR. ACM, 2623--2628.

[44]

Hsiang-Fu Yu, Mikhail Bilenko, and Chih-Jen Lin. 2017. Selection of negative samples for one-class matrix factorization. In SDM. SIAM, 363--371.

[45]

Runtian Zhai, Chen Dan, Zico Kolter, and Pradeep Ravikumar. 2021. DORO: distributional and outlier robust optimization. In ICML. PMLR, 12345--12355.

[46]

An Zhang, Wenchang Ma, Xiang Wang, and Tat seng Chua. 2022. Incorporating Bias-aware Margins into Contrastive Loss for Collaborative Filtering. In NeurIPS. Curran Associates, Inc.

[47]

An Zhang, Jingnan Zheng, Xiang Wang, Yancheng Yuan, and Tat-Seng Chua. 2023 b. Invariant Collaborative Filtering to Popularity Distribution Shift. In WWW. ACM, 1240--1251.

[48]

Jizhi Zhang, Keqin Bao, Yang Zhang, Wenjie Wang, Fuli Feng, and Xiangnan He. 2023 a. Is ChatGPT Fair for Recommendation? Evaluating Fairness in Large Language Model Recommendation. In RecSys. ACM.

[49]

Shengyu Zhang, Dong Yao, Zhou Zhao, Tat-Seng Chua, and Fei Wu. 2021b. CauseRec: Counterfactual User Sequence Synthesis for Sequential Recommendation. In SIGIR. ACM, 367--377.

[50]

Yang Zhang, Fuli Feng, Xiangnan He, Tianxin Wei, Chonggang Song, Guohui Ling, and Yongdong Zhang. 2021a. Causal intervention for leveraging popularity bias in recommendation. In SIGIR. ACM, 11--20.

[51]

Yu Zheng, Chen Gao, Xiang Li, Xiangnan He, Yong Li, and Depeng Jin. 2020. Disentangling user interest and popularity bias for recommendation with causal embedding. arXiv:2006.11011 (2020).

[52]

Chunting Zhou, Xuezhe Ma, Paul Michel, and Graham Neubig. 2021. Examining and combating spurious features under distribution shift. In ICML. PMLR, 12857--12867.

[53]

Ziwei Zhu, Yun He, Xing Zhao, and James Caverlee. 2021a. Popularity bias in dynamic recommendation. In KDD. ACM, 2439--2449.

[54]

Ziwei Zhu, Yun He, Xing Zhao, Yin Zhang, Jianling Wang, and James Caverlee. 2021b. Popularity-opportunity bias in collaborative filtering. In WSDM. ACM, 85--93.

Cited By

Zhao JWenjie WXu YSun TFeng FChua THui Yang GWang HHan SHauff CZuccon GZhang Y(2024)Denoising Diffusion Recommender ModelProceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3626772.3657825(1370-1379)Online publication date: 10-Jul-2024
https://dl.acm.org/doi/10.1145/3626772.3657825

Index Terms

Popularity-aware Distributionally Robust Optimization for Recommendation System
1. Information systems
  1. Information retrieval
    1. Retrieval tasks and goals
      1. Recommender systems

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cover image ACM Conferences

CIKM '23: Proceedings of the 32nd ACM International Conference on Information and Knowledge Management

October 2023

5508 pages

ISBN:9798400701245

DOI:10.1145/3583780

General Chairs:
Ingo Frommholz
University of Wolverhampton, UK
,
Frank Hopfgartner
University of Koblenz, Germany
,
Mark Lee
University of Birmingham, UK
,
Michael Oakes
University of Birmingham, UK
,
Program Chairs:
Mounia Lalmas
Spotify, UK
,
Min Zhang
Tsinghua University, China
,
Rodrygo Santos
Federal University of Minas Gerais, Brazil

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Published: 21 October 2023

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CIKM '23: The 32nd ACM International Conference on Information and Knowledge Management

October 21 - 25, 2023

Birmingham, United Kingdom

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Cited By

Zhao JWenjie WXu YSun TFeng FChua THui Yang GWang HHan SHauff CZuccon GZhang Y(2024)Denoising Diffusion Recommender ModelProceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3626772.3657825(1370-1379)Online publication date: 10-Jul-2024
https://dl.acm.org/doi/10.1145/3626772.3657825

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