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research-article

Bias and Debias in Recommender System: A Survey and Future Directions

Authors:

Xiangnan HeAuthors Info & Claims

ACM Transactions on Information Systems, Volume 41, Issue 3

Article No.: 67, Pages 1 - 39

https://doi.org/10.1145/3564284

Published: 07 February 2023 Publication History

Abstract

While recent years have witnessed a rapid growth of research papers on recommender system (RS), most of the papers focus on inventing machine learning models to better fit user behavior data. However, user behavior data is observational rather than experimental. This makes various biases widely exist in the data, including but not limited to selection bias, position bias, exposure bias, and popularity bias. Blindly fitting the data without considering the inherent biases will result in many serious issues, e.g., the discrepancy between offline evaluation and online metrics, hurting user satisfaction and trust on the recommendation service, and so on. To transform the large volume of research models into practical improvements, it is highly urgent to explore the impacts of the biases and perform debiasing when necessary. When reviewing the papers that consider biases in RS, we find that, to our surprise, the studies are rather fragmented and lack a systematic organization. The terminology “bias” is widely used in the literature, but its definition is usually vague and even inconsistent across papers. This motivates us to provide a systematic survey of existing work on RS biases. In this paper, we first summarize seven types of biases in recommendation, along with their definitions and characteristics. We then provide a taxonomy to position and organize the existing work on recommendation debiasing. Finally, we identify some open challenges and envision some future directions, with the hope of inspiring more research work on this important yet less investigated topic. The summary of debiasing methods reviewed in this survey can be found at https://github.com/jiawei-chen/RecDebiasing.

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Index Terms

Bias and Debias in Recommender System: A Survey and Future Directions
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cover image ACM Transactions on Information Systems

ACM Transactions on Information Systems Volume 41, Issue 3

July 2023

890 pages

ISSN:1046-8188

EISSN:1558-2868

DOI:10.1145/3582880

Editor:
Min Zhang
Tsinghua University, China

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Publication History

Published: 07 February 2023

Online AM: 19 October 2022

Accepted: 15 August 2022

Revised: 08 July 2022

Received: 28 December 2021

Published in TOIS Volume 41, Issue 3

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