research-article

Mitigating Sentiment Bias for Recommender Systems

Authors:

Hui LiAuthors Info & Claims

SIGIR '21: Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval

Pages 31 - 40

https://doi.org/10.1145/3404835.3462943

Published: 11 July 2021 Publication History

Abstract

Biases and de-biasing in recommender systems (RS) have become a research hotspot recently. This paper reveals an unexplored type of bias, i.e., sentiment bias. Through an empirical study, we find that many RS models provide more accurate recommendations on user/item groups having more positive feedback (i.e., positive users/items) than on user/item groups having more negative feedback (i.e., negative users/items). We show that sentiment bias is different from existing biases such as popularity bias: positive users/items do not have more user feedback (i.e., either more ratings or longer reviews). The existence of sentiment bias leads to low-quality recommendations to critical users and unfair recommendations for niche items. We discuss the factors that cause sentiment bias. Then, to fix the sources of sentiment bias, we propose a general de-biasing framework with three strategies manifesting in different regularizers that can be easily plugged into RS models without changing model architectures. Experiments on various RS models and benchmark datasets have verified the effectiveness of our de-biasing framework. To our best knowledge, sentiment bias and its de-biasing have not been studied before. We hope that this work can help strengthen the study of biases and de-biasing in RS.

Supplementary Material

MP4 File (fp0792.mp4)

presentation video

Download
31.42 MB

References

[1]

Himan Abdollahpouri, Robin Burke, and Bamshad Mobasher. 2017. Controlling Popularity Bias in Learning-to-Rank Recommendation. In RecSys. 42--46.

[2]

Himan Abdollahpouri, Robin Burke, and Bamshad Mobasher. 2019 a. Managing Popularity Bias in Recommender Systems with Personalized Re-Ranking. In FLAIRS Conference. 413--418.

[3]

Himan Abdollahpouri and Masoud Mansoury. 2020. Multi-sided Exposure Bias in Recommendation. In IRS2020@KDD .

[4]

Himan Abdollahpouri, Masoud Mansoury, Robin Burke, and Bamshad Mobasher. 2019 b. The Unfairness of Popularity Bias in Recommendation. In RMSE@RecSys, Vol. 2440.

[5]

Himan Abdollahpouri, Masoud Mansoury, Robin Burke, and Bamshad Mobasher. 2020. The Connection Between Popularity Bias, Calibration, and Fairness in Recommendation. In RecSys. 726--731.

[6]

Charu C. Aggarwal. 2016. Recommender Systems - The Textbook .Springer.

Digital Library

[7]

Stephen Bonner and Flavian Vasile. 2018. Causal embeddings for recommendation. In RecSys. 104--112.

[8]

Chong Chen, Min Zhang, Yiqun Liu, and Shaoping Ma. 2018. Neural Attentional Rating Regression with Review-level Explanations. In WWW. 1583--1592.

[9]

Jiawei Chen, Hande Dong, Xiang Wang, Fuli Feng, Meng Wang, and Xiangnan He. 2020 a. Bias and Debias in Recommender System: A Survey and Future Directions. arXiv Preprint (2020). https://arxiv.org/abs/2010.03240

[10]

Zhihong Chen, Rong Xiao, Chenliang Li, Gangfeng Ye, Haochuan Sun, and Hongbo Deng. 2020 b. ESAM: Discriminative Domain Adaptation with Non-Displayed Items to Improve Long-Tail Performance. In SIGIR. 579--588.

[11]

Andrew Collins, Dominika Tkaczyk, Akiko Aizawa, and Jö ran Beel. 2018. A Study of Position Bias in Digital Library Recommender Systems. arXiv Preprint (2018). https://arxiv.org/abs/1802.06565

[12]

Alberto Garc'i a-Durá n, Roberto Gonzalez, Daniel O n oro-Rubio, Mathias Niepert, and Hui Li. 2020. TransRev: Modeling Reviews as Translations from Users to Items. In ECIR, Vol. 12035. 234--248.

[13]

David Godes and Jose C. Silva. 2012. Sequential and Temporal Dynamics of Online Opinion. Marketing Science, Vol. 31, 3 (2012), 448--473.

Digital Library

[14]

Huifeng Guo, Jinkai Yu, Qing Liu, Ruiming Tang, and Yuzhou Zhang. 2019. PAL: a position-bias aware learning framework for CTR prediction in live recommender systems. In RecSys. 452--456.

[15]

Ruining He, Wang-Cheng Kang, and Julian J. McAuley. 2017a. Translation-based Recommendation. In RecSys. 161--169.

[16]

Ruining He, Wang-Cheng Kang, and Julian J. McAuley. 2018. Translation-based Recommendation: A Scalable Method for Modeling Sequential Behavior. In IJCAI. 5264--5268.

[17]

Xiangnan He, Lizi Liao, Hanwang Zhang, Liqiang Nie, Xia Hu, and Tat-Seng Chua. 2017b. Neural Collaborative Filtering. In WWW. 173--182.

[18]

Katja Hofmann, Anne Schuth, Alejandro Bellog'i n, and Maarten de Rijke. 2014. Effects of Position Bias on Click-Based Recommender Evaluation. In ECIR, Vol. 8416. 624--630.

[19]

Dongmin Hyun, Chanyoung Park, Min-Chul Yang, Ilhyeon Song, Jung-Tae Lee, and Hwanjo Yu. 2018. Review Sentiment-Guided Scalable Deep Recommender System. In SIGIR. 965--968.

[20]

Yehuda Koren, Robert M. Bell, and Chris Volinsky. 2009. Matrix Factorization Techniques for Recommender Systems. Computer, Vol. 42, 8 (2009), 30--37.

Digital Library

[21]

Dominik Kowald, Markus Schedl, and Elisabeth Lex. 2020. The Unfairness of Popularity Bias in Music Recommendation: A Reproducibility Study. In ECIR, Vol. 12036. 35--42.

Digital Library

[22]

Sanjay Krishnan, Jay Patel, Michael J. Franklin, and Ken Goldberg. 2014. A methodology for learning, analyzing, and mitigating social influence bias in recommender systems. In RecSys. 137--144.

[23]

Hui Li, Ye Liu, Nikos Mamoulis, and David S. Rosenblum. 2020. Translation-Based Sequential Recommendation for Complex Users on Sparse Data. IEEE Trans. Knowl. Data Eng., Vol. 32, 8 (2020), 1639--1651.

[24]

Dawen Liang, Laurent Charlin, James McInerney, and David M. Blei. 2016. Modeling User Exposure in Recommendation. In WWW. 951--961.

Digital Library

[25]

Dugang Liu, Pengxiang Cheng, Zhenhua Dong, Xiuqiang He, Weike Pan, and Zhong Ming. 2020 a. A General Knowledge Distillation Framework for Counterfactual Recommendation via Uniform Data. In SIGIR. 831--840.

[26]

Donghua Liu, Jing Li, Bo Du, Jun Chang, and Rong Gao. 2019. DAML: Dual Attention Mutual Learning between Ratings and Reviews for Item Recommendation. In KDD. 344--352.

[27]

Hongtao Liu, Wenjun Wang, Hongyan Xu, Qiyao Peng, and Pengfei Jiao. 2020 b. Neural Unified Review Recommendation with Cross Attention. In SIGIR. 1789--1792.

[28]

Yiming Liu, Xuezhi Cao, and Yong Yu. 2016. Are You Influenced by Others When Rating?: Improve Rating Prediction by Conformity Modeling. In RecSys. 269--272.

[29]

Hao Ma, Dengyong Zhou, Chao Liu, Michael R. Lyu, and Irwin King. 2011. Recommender systems with social regularization. In WSDM. 287--296.

[30]

Benjamin M. Marlin, Richard S. Zemel, Sam T. Roweis, and Malcolm Slaney. 2007. Collaborative Filtering and the Missing at Random Assumption. In UAI. 267--275.

[31]

Julian J. McAuley, Rahul Pandey, and Jure Leskovec. 2015. Inferring Networks of Substitutable and Complementary Products. In KDD. 785--794.

[32]

Ninareh Mehrabi, Fred Morstatter, Nripsuta Saxena, Kristina Lerman, and Aram Galstyan. 2019. A Survey on Bias and Fairness in Machine Learning. arXiv Preprint (2019). https://arxiv.org/abs/1908.09635

[33]

Rajiv Pasricha and Julian J. McAuley. 2018. Translation-based factorization machines for sequential recommendation. In RecSys. 63--71.

[34]

Steffen Rendle, Walid Krichene, Li Zhang, and John R. Anderson. 2020. Neural Collaborative Filtering vs. Matrix Factorization Revisited. In RecSys. 240--248.

Digital Library

[35]

Noveen Sachdeva and Julian J. McAuley. 2020. How Useful are Reviews for Recommendation? A Critical Review and Potential Improvements. In SIGIR. 1845--1848.

[36]

Yuta Saito. 2020. Asymmetric Tri-training for Debiasing Missing-Not-At-Random Explicit Feedback. In SIGIR. 309--318.

[37]

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

[38]

Harald Steck. 2010. Training and testing of recommender systems on data missing not at random. In KDD. 713--722.

[39]

Tony Sun, Andrew Gaut, Shirlyn Tang, Yuxin Huang, Mai ElSherief, Jieyu Zhao, Diba Mirza, Elizabeth M. Belding, Kai-Wei Chang, and William Yang Wang. 2019. Mitigating Gender Bias in Natural Language Processing: Literature Review. In ACL. 1630--1640.

[40]

Yi Tay, Anh Tuan Luu, and Siu Cheung Hui. 2018. Multi-Pointer Co-Attention Networks for Recommendation. In KDD. 2309--2318.

Digital Library

[41]

Laurens van der Maaten and Geoffrey Hinton. 2008. Visualizing Data using t-SNE. J. Mach. Learn. Res., Vol. 9 (2008), 2579--2605.

[42]

Mei Wang and Weihong Deng. 2020. Mitigating Bias in Face Recognition Using Skewness-Aware Reinforcement Learning. In CVPR. 9319--9328.

[43]

Quan Wang, Zhendong Mao, Bin Wang, and Li Guo. 2017. Knowledge Graph Embedding: A Survey of Approaches and Applications. IEEE Trans. Knowl. Data Eng., Vol. 29, 12 (2017), 2724--2743.

[44]

Ting Wang and Dashun Wang. 2014. Why Amazon's Ratings Might Mislead You: The Story of Herding Effects. Big Data, Vol. 2, 4 (2014), 196--204.

[45]

Yixin Wang, Dawen Liang, Laurent Charlin, and David M. Blei. 2018. The Deconfounded Recommender: A Causal Inference Approach to Recommendation. arXiv Preprint (2018). https://arxiv.org/abs/1808.06581

[46]

Yixin Wang, Dawen Liang, Laurent Charlin, and David M. Blei. 2020. Causal Inference for Recommender Systems. In RecSys. 426--431.

[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. arXiv Preprint (2020). https://arxiv.org/abs/2010.15363

[48]

Yin Zhang, Yun He, Jianling Wang, and James Caverlee. 2020. Adaptive Hierarchical Translation-based Sequential Recommendation. In WWW. 2984--2990.

[49]

Hua Zheng, Dong Wang, Qi Zhang, Hang Li, and Tinghao Yang. 2010. Do clicks measure recommendation relevancy?: an empirical user study. In RecSys. 249--252.

[50]

Lei Zheng, Vahid Noroozi, and Philip S. Yu. 2017. Joint Deep Modeling of Users and Items Using Reviews for Recommendation. In WSDM. 425--434.

Cited By

Yang HWu XQiu ZZheng YChen X(2024)Distributional Fairness-aware RecommendationACM Transactions on Information Systems10.1145/365285442:5(1-28)Online publication date: 29-Apr-2024
https://dl.acm.org/doi/10.1145/3652854
Rahmani HNaghiaei MDeldjoo Y(2024)A Personalized Framework for Consumer and Producer Group Fairness Optimization in Recommender SystemsACM Transactions on Recommender Systems10.1145/36511672:3(1-24)Online publication date: 5-Jun-2024
https://dl.acm.org/doi/10.1145/3651167
Gao CZheng YWang WFeng FHe XLi Y(2024)Causal Inference in Recommender Systems: A Survey and Future DirectionsACM Transactions on Information Systems10.1145/363904842:4(1-32)Online publication date: 9-Feb-2024
https://dl.acm.org/doi/10.1145/3639048
Show More Cited By

Index Terms

Mitigating Sentiment Bias for Recommender Systems
1. Information systems
  1. Information retrieval
    1. Retrieval tasks and goals
      1. Recommender systems

Recommendations

Causal Intervention for Sentiment De-biasing in Recommendation
CIKM '22: Proceedings of the 31st ACM International Conference on Information & Knowledge Management

Biases and de-biasing in recommender systems have received increasing attention recently. This study focuses on a newly identified bias, i.e., sentiment bias, which is defined as the divergence in recommendation performance between positive users/items ...
Measuring and Mitigating Item Under-Recommendation Bias in Personalized Ranking Systems
SIGIR '20: Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval

Recommendation algorithms typically build models based on user-item interactions (e.g., clicks, likes, or ratings) to provide a personalized ranked list of items. These interactions are often distributed unevenly over different groups of items due to ...
Acquiring User Information Needs for Recommender Systems
WI-IAT '13: Proceedings of the 2013 IEEE/WIC/ACM International Joint Conferences on Web Intelligence (WI) and Intelligent Agent Technologies (IAT) - Volume 03

Most recommender systems attempt to use collaborative filtering, content-based filtering or hybrid approach to recommend items to new users. Collaborative filtering recommends items to new users based on their similar neighbours, and content-based ...

Comments

Information & Contributors

Information

Published In

cover image ACM Conferences

SIGIR '21: Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval

July 2021

2998 pages

ISBN:9781450380379

DOI:10.1145/3404835

General Chairs:
Fernando Diaz
(Google)
,
Chirag Shah
University of Washington
,
Torsten Suel
New York University
,
Program Chairs:
Pablo Castells
Universidad Autónoma de Madrid, Amazon
,
Rosie Jones
Spotify
,
Tetsuya Sakai
Waseda University

Copyright © 2021 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

Sponsors

SIGIR: ACM Special Interest Group on Information Retrieval

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 11 July 2021

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Research-article

Funding Sources

Joint Innovation Research Program of Fujian Province China
Natural Science Foundation of Fujian Province China
Natural Science Foundation of China

Conference

SIGIR '21

Sponsor:

SIGIR

SIGIR '21: The 44th International ACM SIGIR Conference on Research and Development in Information Retrieval

July 11 - 15, 2021

Virtual Event, Canada

Acceptance Rates

Overall Acceptance Rate 792 of 3,983 submissions, 20%

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

15
Total Citations
View Citations
1,013
Total Downloads

Downloads (Last 12 months)135
Downloads (Last 6 weeks)11

Reflects downloads up to 27 Jul 2024

Other Metrics

View Author Metrics

Citations

Cited By

Yang HWu XQiu ZZheng YChen X(2024)Distributional Fairness-aware RecommendationACM Transactions on Information Systems10.1145/365285442:5(1-28)Online publication date: 29-Apr-2024
https://dl.acm.org/doi/10.1145/3652854
Rahmani HNaghiaei MDeldjoo Y(2024)A Personalized Framework for Consumer and Producer Group Fairness Optimization in Recommender SystemsACM Transactions on Recommender Systems10.1145/36511672:3(1-24)Online publication date: 5-Jun-2024
https://dl.acm.org/doi/10.1145/3651167
Gao CZheng YWang WFeng FHe XLi Y(2024)Causal Inference in Recommender Systems: A Survey and Future DirectionsACM Transactions on Information Systems10.1145/363904842:4(1-32)Online publication date: 9-Feb-2024
https://dl.acm.org/doi/10.1145/3639048
Cavenaghi EZanga AStella FZanker M(2024)Towards a Causal Decision-Making Framework for Recommender SystemsACM Transactions on Recommender Systems10.1145/36291692:2(1-34)Online publication date: 14-May-2024
https://dl.acm.org/doi/10.1145/3629169
Zhao LLiu QYue LChen WChen LSun RSong CHui Yang GWang HHan SHauff CZuccon GZhang Y(2024)COMI: COrrect and MItigate Shortcut Learning Behavior in Deep Neural NetworksProceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3626772.3657729(218-228)Online publication date: 10-Jul-2024
https://dl.acm.org/doi/10.1145/3626772.3657729
Yang ZWang YCheng YZhang TWang X(2023)Recommendation Model Based on Enhanced Graph Convolution That Fuses Review PropertiesIEEE Transactions on Computational Social Systems10.1109/TCSS.2022.318106510:5(2266-2278)Online publication date: Oct-2023
https://doi.org/10.1109/TCSS.2022.3181065
Shinde SGaikwad MAnsari HKale PLunawat S(2023)Bias Mitigation in News Recommender Systems Using Preference Correction2023 7th International Conference On Computing, Communication, Control And Automation (ICCUBEA)10.1109/ICCUBEA58933.2023.10392104(1-6)Online publication date: 18-Aug-2023
https://doi.org/10.1109/ICCUBEA58933.2023.10392104
Kumar DKumar DSingh SChhikara G(2023)BigBasket Fairness Analysis for Searched Outputs2023 14th International Conference on Computing Communication and Networking Technologies (ICCCNT)10.1109/ICCCNT56998.2023.10307520(1-6)Online publication date: 6-Jul-2023
https://doi.org/10.1109/ICCCNT56998.2023.10307520
Amigó EDeldjoo YMizzaro SBellogín A(2023)A unifying and general account of fairness measurement in recommender systemsInformation Processing and Management: an International Journal10.1016/j.ipm.2022.10311560:1Online publication date: 1-Jan-2023
https://dl.acm.org/doi/10.1016/j.ipm.2022.103115
Deldjoo YJannach DBellogin ADifonzo AZanzonelli D(2023)Fairness in recommender systems: research landscape and future directionsUser Modeling and User-Adapted Interaction10.1007/s11257-023-09364-z34:1(59-108)Online publication date: 24-Apr-2023
https://dl.acm.org/doi/10.1007/s11257-023-09364-z
Show More Cited By

View Options

Get Access

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Publication

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

Media

Figures

Other

Tables

View Table of Contents