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Fighting Mainstream Bias in Recommender Systems via Local Fine Tuning

Published: 15 February 2022 Publication History

Abstract

In collaborative filtering, the quality of recommendations critically relies on how easily a model can find similar users for a target user. Hence, a niche user who prefers items out of the mainstream may receive poor recommendations, while a mainstream user sharing interests with many others will likely receive recommendations of higher quality. In this work, we study this mainstream bias centering around three key thrusts. First, to distinguish mainstream and niche users, we explore four approaches based on outlier detection techniques to identify a mainstream score indicating the mainstream level for each user. Second, we empirically show that severe mainstream bias is produced by conventional recommendation models. Last, we explore both global and local methods to mitigate the bias. Concretely, we propose two global models: Distribution Calibration (DC) and Weighted Loss (WL) methods; and one local method: Local Fine Tuning (LFT) method. Extensive experiments show the effectiveness of the proposed methods to improve utility for niche users and also show that the proposed LFT can improve the utility for mainstream users at the same time.

References

[1]
Himan Abdollahpouri, Robin Burke, and Bamshad Mobasher. 2017. Controlling popularity bias in learning-to-rank recommendation. In Proceedings of the eleventh ACM conference on recommender systems. 42--46.
[2]
Himan Abdollahpouri, Robin Burke, and Bamshad Mobasher. 2019. Managing popularity bias in recommender systems with personalized re-ranking. In The thirty-second international flairs conference .
[3]
Irad Ben-Gal. 2005. Outlier detection. In Data mining and knowledge discovery handbook. Springer, 131--146.
[4]
Alex Beutel, Jilin Chen, Tulsee Doshi, Hai Qian, Li Wei, Yi Wu, Lukasz Heldt, Zhe Zhao, Lichan Hong, Ed H Chi, et al. 2019. Fairness in recommendation ranking through pairwise comparisons. In Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining . 2212--2220.
[5]
Markus M Breunig, Hans-Peter Kriegel, Raymond T Ng, and Jörg Sander. 2000. LOF: identifying density-based local outliers. In Proceedings of the 2000 ACM SIGMOD international conference on Management of data. 93--104.
[6]
Minjin Choi, Yoonki Jeong, Joonseok Lee, and Jongwuk Lee. 2021. Local Collaborative Autoencoders. In Proceedings of the 14th ACM International Conference on Web Search and Data Mining. 734--742.
[7]
Evangelia Christakopoulou and George Karypis. 2018. Local latent space models for top-n recommendation. In Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining . 1235--1243.
[8]
Paolo Cremonesi, Yehuda Koren, and Roberto Turrin. 2010. Performance of recommender algorithms on top-n recommendation tasks. In Proceedings of the fourth ACM conference on Recommender systems. 39--46.
[9]
Michael D Ekstrand, Mucun Tian, Ion Madrazo Azpiazu, Jennifer D Ekstrand, Oghenemaro Anuyah, David McNeill, and Maria Soledad Pera. 2018. All the cool kids, how do they fit in?: Popularity and demographic biases in recommender evaluation and effectiveness. In Conference on fairness, accountability and transparency. PMLR, 172--186.
[10]
Chelsea Finn, Pieter Abbeel, and Sergey Levine. 2017. Model-agnostic meta-learning for fast adaptation of deep networks. In International Conference on Machine Learning. PMLR, 1126--1135.
[11]
Zuohui Fu, Yikun Xian, Ruoyuan Gao, Jieyu Zhao, Qiaoying Huang, Yingqiang Ge, Shuyuan Xu, Shijie Geng, Chirag Shah, Yongfeng Zhang, et al. 2020. Fairness-aware explainable recommendation over knowledge graphs. In Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval. 69--78.
[12]
Sahin Cem Geyik, Stuart Ambler, and Krishnaram Kenthapadi. 2019. Fairness-aware ranking in search & recommendation systems with application to linkedin talent search. In Proceedings of the 25th acm sigkdd international conference on knowledge discovery & data mining . 2221--2231.
[13]
F Maxwell Harper and Joseph A Konstan. 2015. The movielens datasets: History and context. Acm transactions on interactive intelligent systems (tiis), Vol. 5, 4 (2015), 1--19.
[14]
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. 173--182.
[15]
Diederik P Kingma and Jimmy Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 (2014).
[16]
Yehuda Koren, Robert Bell, and Chris Volinsky. 2009. Matrix factorization techniques for recommender systems. Computer, Vol. 42, 8 (2009), 30--37.
[17]
Joonseok Lee, Seungyeon Kim, Guy Lebanon, and Yoram Singer. 2013. Local low-rank matrix approximation. In International conference on machine learning . PMLR, 82--90.
[18]
Joonseok Lee, Seungyeon Kim, Guy Lebanon, Yoram Singer, and Samy Bengio. 2016. LLORMA: Local low-rank matrix approximation. (2016).
[19]
Jae-woong Lee, Seongmin Park, and Jongwuk Lee. 2021. Dual Unbiased Recommender Learning for Implicit Feedback. In Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval . 1647--1651.
[20]
Roger Zhe Li, Julián Urbano, and Alan Hanjalic. 2021 b. Leave No User Behind: Towards Improving the Utility of Recommender Systems for Non-mainstream Users. In Proceedings of the 14th ACM International Conference on Web Search and Data Mining. 103--111.
[21]
Yunqi Li, Hanxiong Chen, Zuohui Fu, Yingqiang Ge, and Yongfeng Zhang. 2021 a. User-oriented Fairness in Recommendation. In Proceedings of the Web Conference 2021 . 624--632.
[22]
Dawen Liang, Rahul G Krishnan, Matthew D Hoffman, and Tony Jebara. 2018. Variational autoencoders for collaborative filtering. In Proceedings of the 2018 world wide web conference. 689--698.
[23]
Weiwen Liu and Robin Burke. 2018. Personalizing fairness-aware re-ranking. arXiv preprint arXiv:1809.02921 (2018).
[24]
Alex Nichol, Joshua Achiam, and John Schulman. 2018. On first-order meta-learning algorithms. arXiv preprint arXiv:1803.02999 (2018).
[25]
Adam Paszke, Sam Gross, Francisco Massa, Adam Lerer, James Bradbury, Gregory Chanan, Trevor Killeen, Zeming Lin, Natalia Gimelshein, Luca Antiga, et al. 2019. Pytorch: An imperative style, high-performance deep learning library. Advances in neural information processing systems, Vol. 32 (2019), 8026--8037.
[26]
Steffen Rendle, Christoph Freudenthaler, Zeno Gantner, and Lars Schmidt-Thieme. 2012. BPR: Bayesian personalized ranking from implicit feedback. arXiv preprint arXiv:1205.2618 (2012).
[27]
Lukas Ruff, Robert Vandermeulen, Nico Goernitz, Lucas Deecke, Shoaib Ahmed Siddiqui, Alexander Binder, Emmanuel Müller, and Marius Kloft. 2018. Deep one-class classification. In International conference on machine learning . PMLR, 4393--4402.
[28]
Yuta Saito. 2020. Unbiased Pairwise Learning from Biased Implicit Feedback. In Proceedings of the 2020 ACM SIGIR on International Conference on Theory of Information Retrieval . 5--12.
[29]
Yuta Saito, Suguru Yaginuma, Yuta Nishino, Hayato Sakata, and Kazuhide Nakata. 2020. Unbiased recommender learning from missing-not-at-random implicit feedback. In Proceedings of the 13th International Conference on Web Search and Data Mining. 501--509.
[30]
Markus Schedl and Christine Bauer. 2019. Online music listening culture of kids and adolescents: Listening analysis and music recommendation tailored to the young. arXiv preprint arXiv:1912.11564 (2019).
[31]
Noam Shazeer, Azalia Mirhoseini, Krzysztof Maziarz, Andy Davis, Quoc Le, Geoffrey Hinton, and Jeff Dean. 2017. Outrageously large neural networks: The sparsely-gated mixture-of-experts layer. arXiv preprint arXiv:1701.06538 (2017).
[32]
Jiliang Tang, Huiji Gao, and Huan Liu. 2012. mTrust: discerning multi-faceted trust in a connected world. In Proceedings of the 5th WSDM .
[33]
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.
[34]
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. arXiv preprint arXiv:2010.15363 (2020).
[35]
Shuo Yang, Lu Liu, and Min Xu. 2021. Free lunch for few-shot learning: Distribution calibration. arXiv preprint arXiv:2101.06395 (2021).
[36]
Sirui Yao and Bert Huang. 2017. Beyond parity: Fairness objectives for collaborative filtering. arXiv preprint arXiv:1705.08804 (2017).
[37]
Yin Zhang, Derek Zhiyuan Cheng, Tiansheng Yao, Xinyang Yi, Lichan Hong, and Ed H Chi. 2021 a. A Model of Two Tales: Dual Transfer Learning Framework for Improved Long-tail Item Recommendation. In Proceedings of the Web Conference 2021 . 2220--2231.
[38]
Yang Zhang, Fuli Feng, Xiangnan He, Tianxin Wei, Chonggang Song, Guohui Ling, and Yongdong Zhang. 2021 b. Causal Intervention for Leveraging Popularity Bias in Recommendation. arXiv preprint arXiv:2105.06067 (2021).
[39]
Xing Zhao, Ziwei Zhu, Majid Alfifi, and James Caverlee. 2020. Addressing the Target Customer Distortion Problem in Recommender Systems. In Proceedings of The Web Conference 2020. 2969--2975.
[40]
Ziwei Zhu, Yun He, Yin Zhang, and James Caverlee. 2020 a. Unbiased Implicit Recommendation and Propensity Estimation via Combinational Joint Learning. In Fourteenth ACM Conference on Recommender Systems . 551--556.
[41]
Ziwei Zhu, Yun He, Xing Zhao, Yin Zhang, Jianling Wang, and James Caverlee. 2021. Popularity-Opportunity Bias in Collaborative Filtering. In Proceedings of the 14th ACM International Conference on Web Search and Data Mining . 85--93.
[42]
Ziwei Zhu, Jianling Wang, and James Caverlee. 2020 b. Measuring and mitigating item under-recommendation bias in personalized ranking systems. In Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval . 449--458.

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  • (2024)Improving Recommendations for Non-Mainstream Users by Addressing Subjective Item ViewsAdjunct Proceedings of the 32nd ACM Conference on User Modeling, Adaptation and Personalization10.1145/3631700.3664916(35-39)Online publication date: 27-Jun-2024
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    cover image ACM Conferences
    WSDM '22: Proceedings of the Fifteenth ACM International Conference on Web Search and Data Mining
    February 2022
    1690 pages
    ISBN:9781450391320
    DOI:10.1145/3488560
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    Published: 15 February 2022

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    Author Tags

    1. local models
    2. mainstream bias
    3. recommender systems

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    • (2024)Improving Recommendations for Non-Mainstream Users by Addressing Subjective Item ViewsAdjunct Proceedings of the 32nd ACM Conference on User Modeling, Adaptation and Personalization10.1145/3631700.3664916(35-39)Online publication date: 27-Jun-2024
    • (2024)Learning-to-rank debias with popularity-weighted negative sampling and popularity regularizationNeurocomputing10.1016/j.neucom.2024.127681587(127681)Online publication date: Jun-2024
    • (2024)Neural_BPRElectronic Commerce Research and Applications10.1016/j.elerap.2023.10132362:COnline publication date: 4-Mar-2024
    • (2024)Countering Mainstream Bias via End-to-End Adaptive Local LearningAdvances in Information Retrieval10.1007/978-3-031-56069-9_6(75-89)Online publication date: 24-Mar-2024
    • (2023)Multi-Behavior Job Recommendation with Dynamic AvailabilityProceedings of the Annual International ACM SIGIR Conference on Research and Development in Information Retrieval in the Asia Pacific Region10.1145/3624918.3625314(264-271)Online publication date: 26-Nov-2023
    • (2023)Relieving Popularity Bias in Interactive Recommendation: A Diversity-Novelty-Aware Reinforcement Learning ApproachACM Transactions on Information Systems10.1145/361810742:2(1-30)Online publication date: 8-Nov-2023
    • (2023)Collaborative filtering algorithms are prone to mainstream-taste biasProceedings of the 17th ACM Conference on Recommender Systems10.1145/3604915.3608825(750-756)Online publication date: 14-Sep-2023
    • (2023)Auditing Cross-Cultural Consistency of Human-Annotated Labels for Recommendation SystemsProceedings of the 2023 ACM Conference on Fairness, Accountability, and Transparency10.1145/3593013.3594098(1531-1552)Online publication date: 12-Jun-2023
    • (2023)Bias and Debias in Recommender System: A Survey and Future DirectionsACM Transactions on Information Systems10.1145/356428441:3(1-39)Online publication date: 7-Feb-2023
    • (2023)Properly Scoring Users' Mainstreamness to Evaluate Recommendation Bias2023 IEEE International Conference on Systems, Man, and Cybernetics (SMC)10.1109/SMC53992.2023.10394566(3900-3906)Online publication date: 1-Oct-2023
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