Going Beyond Popularity and Positivity Bias: Correcting for Multifactorial Bias in Recommender Systems

Abstract

Two typical forms of bias in user interaction data with recommender systems (RSs) are popularity bias and positivity bias, which manifest themselves as the over-representation of interactions with popular items or items that users prefer, respectively. Debiasing methods aim to mitigate the effect of selection bias on the evaluation and optimization of RSs. However, existing debiasing methods only consider single-factor forms of bias, e.g., only the item (popularity) or only the rating value (positivity). This is in stark contrast with the real world where user selections are generally affected by multiple factors at once. In this work, we consider multifactorial selection bias in RSs. Our focus is on selection bias affected by both item and rating value factors, which is a generalization and combination of popularity and positivity bias. While the concept of multifactorial bias is intuitive, it brings a severe practical challenge as it requires substantially more data for accurate bias estimation. As a solution, we propose smoothing and alternating gradient descent techniques to reduce variance and improve the robustness of its optimization. Our experimental results reveal that, with our proposed techniques, multifactorial bias corrections are more effective and robust than single-factor counterparts on real-world and synthetic datasets.

arXiv commentSIGIR 2024

References in corpus (7)

• Causal Intervention for Leveraging Popularity Bias in Recommendation
• KuaiRec: A Fully-observed Dataset and Insights for Evaluating Recommender Systems
• Correcting for Selection Bias in Learning-to-rank Systems
• It Is Different When Items Are Older: Debiasing Recommendations When Selection Bias and User Preferences Are Dynamic
• Doubly-Robust Estimation for Correcting Position-Bias in Click Feedback for Unbiased Learning to Rank
• Positivity Bias in Customer Satisfaction Ratings
• The Bandwagon Effect: Not Just Another Bias

Cited by in corpus (2)

• Biases in LLM-Generated Musical Taste Profiles for Recommendation
• On the Reliability of Sampling Strategies in Offline Recommender Evaluation