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Mitigating Recommendation Biases via Group-Alignment and Global-Uniformity in Representation Learning

Published: 17 October 2024 Publication History

Abstract

Collaborative Filtering (CF) plays a crucial role in modern recommender systems, leveraging historical user-item interactions to provide personalized suggestions. However, CF-based methods often encounter biases due to imbalances in training data. This phenomenon makes CF-based methods tend to prioritize recommending popular items and performing unsatisfactorily on inactive users. Existing works address this issue by rebalancing training samples, reranking recommendation results, or making the modeling process robust to the bias. Despite their effectiveness, these approaches can compromise accuracy or be sensitive to weighting strategies, making them challenging to train. Therefore, exploring how to mitigate these biases remains in urgent demand.
In this article, we deeply analyze the causes and effects of the biases and propose a framework to alleviate biases in recommendation from the perspective of representation distribution, namely Group-Alignment and Global-Uniformity Enhanced Representation Learning for Debiasing Recommendation (AURL). Specifically, we identify two significant problems in the representation distribution of users and items, namely group-discrepancy and global-collapse. These two problems directly lead to biases in the recommendation results. To this end, we propose two simple but effective regularizers in the representation space, respectively named group-alignment and global-uniformity. The goal of group-alignment is to bring the representation distribution of long-tail entities closer to that of popular entities, while global-uniformity aims to preserve the information of entities as much as possible by evenly distributing representations. Our method directly optimizes both the group-alignment and global-uniformity regularization terms to mitigate recommendation biases. Please note that AURL applies to arbitrary CF-based recommendation backbones. Extensive experiments on three real datasets and various recommendation backbones verify the superiority of our proposed framework. The results show that AURL not only outperforms existing debiasing models in mitigating biases but also improves recommendation performance to some extent.

References

[1]
Shun-ichi Amari. 1993. Backpropagation and stochastic gradient descent method. Neurocomputing 5, 4–5 (1993), 185–196.
[2]
Stephen Bonner and Flavian Vasile. 2018. Causal embeddings for recommendation. In Proceedings of the 12th ACM Conference on Recommender Systems (RecSys). 104–112.
[3]
Sergiy V. Borodachov, Douglas P. Hardin, and Edward B. Saff. 2019. Discrete Energy on Rectifiable Sets. Springer.
[4]
Chong Chen, Min Zhang, Chenyang Wang, Weizhi Ma, Minming Li, Yiqun Liu, and Shaoping Ma. 2019. An efficient adaptive transfer neural network for social-aware recommendation. In Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR). 225–234.
[5]
Jiawei Chen, Hande Dong, Yang Qiu, Xiangnan He, Xin Xin, Liang Chen, Guli Lin, and Keping Yang. 2021. AutoDebias: Learning to debias for recommendation. In Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval. 21–30.
[6]
Jiawei Chen, Hande Dong, Xiang Wang, Fuli Feng, Meng Wang, and Xiangnan He. 2020a. Bias and debias in recommender system: A survey and future directions. ACM Transactions on Information Systems 41, 3 (2020), 1–39.
[7]
Jiajia Chen, Jiancan Wu, Jiawei Chen, Xin Xin, Yong Li, and Xiangnan He. 2024. How graph convolutions amplify popularity bias for recommendation? Frontiers of Computer Science 18, 5 (2024), 185603.
[8]
Jingyuan Chen, Hanwang Zhang, Xiangnan He, Liqiang Nie, Wei Liu, and Tat-Seng Chua. 2017. Attentive collaborative filtering: Multimedia recommendation with item-and component-level attention. In Proceedings of the 40th International ACM SIGIR conference on Research and Development in Information Retrieval. 335–344.
[9]
Lei Chen, Le Wu, Richang Hong, Kun Zhang, and Meng Wang. 2020b. Revisiting graph based collaborative filtering: A linear residual graph convolutional network approach. In Proceedings of the AAAI Conference on Artificial Intelligence. 27–34.
[10]
Lei Chen, Le Wu, Kun Zhang, Richang Hong, Defu Lian, Zhiqiang Zhang, Jun Zhou, and Meng Wang. 2023. Improving recommendation fairness via data augmentation. In Proceedings of the ACM Web Conference (WWW). 1012–1020.
[11]
Anqi Cui, Min Zhang, Yiqun Liu, Shaoping Ma, and Kuo Zhang. 2012. Discover breaking events with popular hashtags in twitter. In Proceedings of the 21st ACM International Conference on Information and Knowledge Management (ICKM). 1794–1798.
[12]
Leyan Deng, Defu Lian, Chenwang Wu, and Enhong Chen. 2022. Graph convolution network based recommender systems: Learning guarantee and item mixture powered strategy. In Proceedings of the Advances in Neural Information Processing Systems (NeurIPS). 3900–3912.
[13]
Chongming Gao, Kexin Huang, Jiawei Chen, Yuan Zhang, Biao Li, Peng Jiang, Shiqi Wang, Zhong Zhang, and Xiangnan He. 2023a. Alleviating Matthew effect of offline reinforcement learning in interactive recommendation. In Annual International ACM SIGIR Conference on Research and Development in Information Retrieval. 238–248.
[14]
Chongming Gao, Shiqi Wang, Shijun Li, Jiawei Chen, Xiangnan He, Wenqiang Lei, Biao Li, Yuan Zhang, and Peng Jiang. 2023b. CIRS: Bursting filter bubbles by counterfactual interactive recommender system. ACM Transactions on Information Systems 42, 1 (2023), 1–27.
[15]
Xavier Glorot and Yoshua Bengio. 2010. Understanding the difficulty of training deep feedforward neural networks. In Proceedings of the 13th International Conference on Artificial Intelligence and Statistics (AISTATS). 249–256.
[16]
Arthur Gretton, Karsten M. Borgwardt, Malte J. Rasch, Bernhard Schölkopf, and Alexander Smola. 2012. A kernel two-sample test. The Journal of Machine Learning Research 13, 1 (2012), 723–773.
[17]
F. Maxwell Harper and Joseph A. Konstan. 2015. The movielens datasets: History and context. ACM Transactions on Interactive Intelligent Systems 5, 4 (2015), 1–19.
[18]
Xiangnan He, Kuan Deng, Xiang Wang, Yan Li, Yongdong Zhang, and Meng Wang. 2020. LightGCN: Simplifying and powering graph convolution network for recommendation. In Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval. 639–648.
[19]
Xiangnan He, Lizi Liao, Hanwang Zhang, Liqiang Nie, Xia Hu, and Tat-Seng Chua. 2017. Neural collaborative filtering. In Proceedings of the 26th International Conference on World Wide Web (WWW). 173–182.
[20]
Min Hou, Le Wu, Enhong Chen, Zhi Li, Vincent W Zheng, and Qi Liu. 2019. Explainable fashion recommendation: A semantic attribute region guided approach. In Proceedings of the International Joint Conference on Artificial Intelligence (IJCAI). 4681–4688.
[21]
Jack Kiefer and Jacob Wolfowitz. 1952. Stochastic estimation of the maximum of a regression function. The Annals of Mathematical Statistics 23, 3 (1952), 462–466.
[22]
Diederik P. Kingma and Jimmy Ba. 2015. Adam: A method for stochastic optimization. In Proceedings of the International Conference on Learning Representations (ICLR).
[23]
Yehuda Koren, Robert M. Bell, and Chris Volinsky. 2009. Matrix factorization techniques for recommender systems. Computer 42, 8 (2009), 30–37.
[24]
Matt J. Kusner, Joshua Loftus, Chris Russell, and Ricardo Silva. 2017. Counterfactual fairness. In Proceedings of the Advances in Neural Information Processing Systems (NeurIPS). 4066–4076.
[25]
Yunqi Li, Hanxiong Chen, Zuohui Fu, Yingqiang Ge, and Yongfeng Zhang. 2021. User-oriented fairness in recommendation. In Proceedings of the Web Conference (WWW). 624–632.
[26]
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 41, 3 (2023), 1–27.
[27]
Mohammadmehdi Naghiaei, Hossein A. Rahmani, and Yashar Deldjoo. 2022. Cpfair: Personalized consumer and producer fairness re-ranking for recommender systems. In Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval. 770–779.
[28]
Matjaz Perc. 2014. The Matthew effect in empirical data. Journal of The Royal Society Interface 11, 98 (2014), 20140378.
[29]
Mohammad Mahfujur Rahman, Clinton Fookes, Mahsa Baktashmotlagh, and Sridha Sridharan. 2020. On minimum discrepancy estimation for deep domain adaptation. In: Richa Singh, Mayank Vatsa, Vishal M. Patel, Nalini Ratha, (Eds.), Domain Adaptation for Visual Understanding. Springer. 81–94.
[30]
Weijieying Ren, Lei Wang, Kunpeng Liu, Ruocheng Guo, Lim Ee Peng, and Yanjie Fu. 2022b. Mitigating popularity bias in recommendation with unbalanced interactions: A gradient perspective. In Proceedings of the IEEE International Conference on Data Mining (ICDM ‘22). 438–447.
[31]
Weijieying Ren, Pengyang Wang, Xiaolin Li, Charles E Hughes, and Yanjie Fu. 2022a. Semi-supervised drifted stream learning with short lookback. In Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining. 1504–1513.
[32]
Steffen Rendle and Christoph Freudenthaler. 2014. Improving pairwise learning for item recommendation from implicit feedback. In Proceedings of the 7th ACM International Conference on Web Search and Data Mining (WSDM). 273–282.
[33]
Steffen Rendle, Christoph Freudenthaler, Zeno Gantner, and Lars Schmidt-Thieme. 2009. BPR: Bayesian personalized ranking from implicit feedback. In Proceedings of the 25th Conference on Uncertainty in Artificial Intelligence (UAI ’09). 452–461.
[34]
Wondo Rhee, Sung Min Cho, and Bongwon Suh. 2022. Countering popularity bias by regularizing score differences. In Proceedings of the 16th ACM Conference on Recommender Systems (RecSys). 145–155.
[35]
Mary Priya Sebastian. 2023. Malayalam natural language processing: Challenges in building a phrase-based statistical machine translation system. ACM Transactions on Asian and Low-Resource Language Information Processing 22, 4 (2023), 1–51.
[36]
Pengyang Shao, Le Wu, Lei Chen, Kun Zhang, and Meng Wang. 2022. FairCF: Fairness-aware collaborative filtering. Science China Information Sciences 65, 2 (2022), 222102.
[37]
Tianxiao Shen, Tao Lei, Regina Barzilay, and Tommi Jaakkola. 2017. Style transfer from non-parallel text by cross-alignment. In Proceedings of the Advances in Neural Information Processing Systems (NeurIPS). 6830–6841.
[38]
Ilya O. Tolstikhin, Bharath K. Sriperumbudur, and Bernhard Schölkopf. 2016. Minimax estimation of maximum mean discrepancy with radial kernels. In Proceedings of the Advances in Neural Information Processing Systems (NeurIPS). 1930–1938.
[39]
Laurens Van der Maaten and Geoffrey Hinton. 2008. Visualizing data using t-SNE. Journal of Machine Learning Research 9, 11 (2008), 2579–2605.
[40]
Thomas Viehmann. 2021. Partial Wasserstein and Maximum Mean Discrepancy distances for bridging the gap between outlier detection and drift detection. arXiv:2106.12893. Retrieved from https://arxiv.org/abs/2106.12893
[41]
Chenyang Wang, Yuanqing Yu, Weizhi Ma, Min Zhang, Chong Chen, Yiqun Liu, and Shaoping Ma. 2022b. Towards representation alignment and uniformity in collaborative filtering. In Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining. 1816–1825.
[42]
Tongzhou Wang and Phillip Isola. 2020. Understanding contrastive representation learning through alignment and uniformity on the hypersphere. In Proceedings of the International Conference on Machine Learning (ICML). 9929–9939.
[43]
Wenjie Wang, Fuli Feng, Xiangnan He, Xiang Wang, and Tat-Seng Chua. 2021. Deconfounded recommendation for alleviating bias amplification. In Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining. 1717–1725.
[44]
Wenjie Wang, Xinyu Lin, Fuli Feng, Xiangnan He, Min Lin, and Tat-Seng Chua. 2022a. Causal representation learning for out-of-distribution recommendation. In Proceedings of the ACM Web Conference (WWW). 3562–3571.
[45]
Xiang Wang, Xiangnan He, Meng Wang, Fuli Feng, and Tat-Seng Chua. 2019. Neural graph collaborative filtering. In Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval. 165–174.
[46]
Yifan Wang, Weizhi Ma, Min Zhang, Yiqun Liu, and Shaoping Ma. 2023. A survey on the fairness of recommender systems. ACM Transactions on Information Systems 41, 3 (2023), 1–43.
[47]
Tianxin Wei, Fuli Feng, Jiawei Chen, Chufeng Shi, Ziwei Wu, Jinfeng Yi, and Xiangnan He. 2020. Model-agnostic counterfactual reasoning for eliminating popularity bias in recommender system. In Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining. 1791–1800.
[48]
Jiancan Wu, Xiangnan He, Xiang Wang, Qifan Wang, Weijian Chen, Jianxun Lian, and Xing Xie. 2022. Graph convolution machine for context-aware recommender system. Frontiers of Computer Science 16, 6 (2022), 166614.
[49]
Jiancan Wu, Xiang Wang, Fuli Feng, Xiangnan He, Liang Chen, Jianxun Lian, and Xing Xie. 2020. Self-supervised graph learning for recommendation. In Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval. 726–735.
[50]
Le Wu, Lei Chen, Pengyang Shao, Richang Hong, Xiting Wang, and Meng Wang. 2021. Learning fair representations for recommendation: A graph-based perspective. In Proceedings of the Web Conference (WWW). 2198–2208.
[51]
Le Wu, Xiangnan He, Xiang Wang, Kun Zhang, and Meng Wang. 2023. A survey on accuracy-oriented neural recommendation: From collaborative filtering to information-rich recommendation. IEEE Transactions on Knowledge and Data Engineering 35, 5 (2023), 4425–4445.
[52]
Yonghui Yang, Le Wu, Kun Zhang, Richang Hong, Hailin Zhou, Zhiqiang Zhang, Jun Zhou, and Meng Wang. 2023b. Hyperbolic graph learning for social recommendation. IEEE Transactions on Knowledge and Data Engineering (2023).
[53]
Yonghui Yang, Zhengwei Wu, Le Wu, Kun Zhang, Richang Hong, Zhiqiang Zhang, Jun Zhou, and Meng Wang. 2023a. Generative-contrastive graph learning for recommendation. In Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval. 1117–1126.
[54]
Junliang Yu, Hongzhi Yin, Xin Xia, Tong Chen, Jundong Li, and Zi Huang. 2023. Self-supervised learning for recommender systems: A survey. IEEE Transactions on Knowledge and Data Engineering 36, 1 (2023), 335–355.
[55]
Junliang Yu, Hongzhi Yin, Xin Xia, Tong Chen, Li zhen Cui, and Quoc Viet Hung Nguyen. 2022. Are graph augmentations necessary?: Simple graph contrastive learning for recommendation. In Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval. 1294–1303.
[56]
An Zhang, Jingnan Zheng, Xiang Wang, Yancheng Yuan, and Tat-Seng Chua. 2023b. Invariant collaborative filtering to popularity distribution shift. In Proceedings of the ACM Web Conference (WWW). 1240–1251.
[57]
Daoan Zhang, Chenming Li, Haoquan Li, Wenjian Huang, Lingyun Huang, and Jianguo Zhang. 2023a. Rethinking alignment and uniformity in unsupervised image semantic segmentation. In Proceedings of the AAAI Conference on Artificial Intelligence. 11192–11200.
[58]
Yang Zhang, Fuli Feng, Xiangnan He, Tianxin Wei, Chonggang Song, Guohui Ling, and Yongdong Zhang. 2021. Causal intervention for leveraging popularity bias in recommendation. In Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval. 11–20.
[59]
Chen Zhao, Le Wu, Pengyang Shao, Kun Zhang, Richang Hong, and Meng Wang. 2023b. Fair representation learning for recommendation: A mutual information perspective. In Proceedings of the AAAI Conference on Artificial Intelligence. 4911–4919.
[60]
Jujia Zhao, Wenjie Wang, Xinyu Lin, Leigang Qu, Jizhi Zhang, and Tat-Seng Chua. 2023a. Popularity-aware distributionally robust optimization for recommendation system. In Proceedings of the 32nd ACM International Conference on Information and Knowledge Management. 4967–4973.
[61]
Minghao Zhao, Le Wu, Yile Liang, Lei Chen, Jian Zhang, Qilin Deng, Kai Wang, Xudong Shen, Tangjie Lv, and Runze Wu. 2022b. Investigating accuracy-novelty performance for graph-based collaborative filtering. In Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval. 50–59.
[62]
Zihao Zhao, Jiawei Chen, Sheng Zhou, Xiangnan He, Xuezhi Cao, Fuzheng Zhang, and Wei Wu. 2022a. Popularity bias is not always evil: Disentangling benign and harmful bias for recommendation. IEEE Transactions on Knowledge and Data Engineering 35, 10 (2022), 9920–9931.
[63]
Yu Zheng, Chen Gao, Xiang Li, Xiangnan He, Yong Li, and Depeng Jin. 2021. Disentangling user interest and conformity for recommendation with causal embedding. In Proceedings of the Web Conference (WWW). 2980–2991.
[64]
Yongchun Zhu, Fuzhen Zhuang, and Deqing Wang. 2019. Aligning domain-specific distribution and classifier for cross-domain classification from multiple sources. In Proceedings of the AAAI Conference on Artificial Intelligence. 5989–5996.
[65]
Ziwei Zhu, Yun He, Xing Zhao, Yin Zhang, Jianling Wang, and James Caverlee. 2021a. Popularity-opportunity bias in collaborative filtering. In Proceedings of the 14th ACM International Conference on Web Search and Data Mining (WSDM). 85–93.
[66]
Zhen Zhu, Tengteng Huang, Mengde Xu, Baoguang Shi, Wenqing Cheng, and Xiang Bai. 2021b. Progressive and aligned pose attention transfer for person image generation. Transactions on Pattern Analysis and Machine Intelligence 44, 8 (2021), 4306–4320.

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  1. Mitigating Recommendation Biases via Group-Alignment and Global-Uniformity in Representation Learning

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    cover image ACM Transactions on Intelligent Systems and Technology
    ACM Transactions on Intelligent Systems and Technology  Volume 15, Issue 5
    October 2024
    719 pages
    EISSN:2157-6912
    DOI:10.1145/3613688
    • Editor:
    • Huan Liu
    Issue’s Table of Contents

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    Association for Computing Machinery

    New York, NY, United States

    Publication History

    Published: 17 October 2024
    Online AM: 14 May 2024
    Accepted: 27 April 2024
    Revised: 06 March 2024
    Received: 20 September 2023
    Published in TIST Volume 15, Issue 5

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    1. Collaborative filtering
    2. representation learning
    3. alignment
    4. uniformity

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    • National Key Research and Development Program of China
    • National Natural Science Foundation of China
    • China Postdoctoral Science Foundation
    • Postdoctoral Fellowship Program of CPSF

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