It Is Different When Items Are Older: Debiasing Recommendations When Selection Bias and User Preferences Are Dynamic

Abstract

User interactions with recommender systems (RSs) are affected by user selection bias, e.g., users are more likely to rate popular items (popularity bias) or items that they expect to enjoy beforehand (positivity bias). Methods exist for mitigating the effects of selection bias in user ratings on the evaluation and optimization of RSs. However, these methods treat selection bias as static, despite the fact that the popularity of an item may change drastically over time and the fact that user preferences may also change over time. We focus on the age of an item and its effect on selection bias and user preferences. Our experimental analysis reveals that the rating behavior of users on the MovieLens dataset is better captured by methods that consider effects from the age of item on bias and preferences. We theoretically show that in a dynamic scenario in which both the selection bias and user preferences are dynamic, existing debiasing methods are no longer unbiased. To address this limitation, we introduce DebiAsing in the dyNamiC scEnaRio (DANCER), a novel debiasing method that extends the inverse propensity scoring debiasing method to account for dynamic selection bias and user preferences. Our experimental results indicate that DANCER improves rating prediction performance compared to debiasing methods that incorrectly assume that selection bias is static in a dynamic scenario. To the best of our knowledge, DANCER is the first debiasing method that accounts for dynamic selection bias and user preferences in RSs.

WSDM 2022

References in corpus (8)

• Disentangled Graph Collaborative Filtering
• Causal Intervention for Leveraging Popularity Bias in Recommendation
• Wide & Deep Learning for Recommender Systems
• Neural Collaborative Filtering
• Bias and Debias in Recommender System: A Survey and Future Directions
• A Re-visit of the Popularity Baseline in Recommender Systems
• RecBole: Towards a Unified, Comprehensive and Efficient Framework for Recommendation Algorithms
• Measuring Recommender System Effects with Simulated Users

Cited by in corpus (5)

• A Survey on Popularity Bias in Recommender Systems
• Going Beyond Popularity and Positivity Bias: Correcting for Multifactorial Bias in Recommender Systems
• A Lightweight Method for Modeling Confidence in Recommendations with Learned Beta Distributions
• Not All Videos Become Outdated: Short-Video Recommendation by Learning to Deconfound Release Interval Bias
• Investigating Characteristics of Media Recommendation Solicitation in r/ifyoulikeblank