A Survey on Popularity Bias in Recommender Systems

Abstract

Recommender systems help people find relevant content in a personalized way. One main promise of such systems is that they are able to increase the visibility of items in the long tail, i.e., the lesser-known items in a catalogue. Existing research, however, suggests that in many situations todays recommendation algorithms instead exhibit a popularity bias, meaning that they often focus on rather popular items in their recommendations. Such a bias may not only lead to the limited value of the recommendations for consumers and providers in the short run, but it may also cause undesired reinforcement effects over time. In this paper, we discuss the potential reasons for popularity bias and review existing approaches to detect, quantify and mitigate popularity bias in recommender systems. Our survey, therefore, includes both an overview of the computational metrics used in the literature as well as a review of the main technical approaches to reduce the bias. Furthermore, we critically discuss todays literature, where we observe that the research is almost entirely based on computational experiments and on certain assumptions regarding the practical effects of including long-tail items in the recommendations.

References in corpus (15)

• Debiased Contrastive Learning for Sequential Recommendation
• User-centered Evaluation of Popularity Bias in Recommender Systems
• Connecting User and Item Perspectives in Popularity Debiasing for Collaborative Recommendation
• Analyzing Item Popularity Bias of Music Recommender Systems: Are Different Genders Equally Affected?
• Quantifying and Mitigating Popularity Bias in Conversational Recommender Systems
• Quantitative analysis of Matthew effect and sparsity problem of recommender systems
• Adap-$\tau$: Adaptively Modulating Embedding Magnitude for Recommendation
• Exploring Longitudinal Effects of Session-based Recommendations
• Leave No User Behind: Towards Improving the Utility of Recommender Systems for Non-mainstream Users
• Using Stable Matching to Optimize the Balance between Accuracy and Diversity in Recommendation
• Auditing the Biases Enacted by YouTube for Political Topics in Germany
• Curse of "Low" Dimensionality in Recommender Systems
• Test Time Embedding Normalization for Popularity Bias Mitigation
• Alleviating the recommendation bias via rank aggregation
• Candidate Set Sampling for Evaluating Top-N Recommendation

Cited by in corpus (7)

• A Survey on Intent-aware Recommender Systems
• Recommender Systems for Good (RS4Good): Survey of Use Cases and a Call to Action for Research that Matters
• Impacts of Mainstream-Driven Algorithms on Recommendations for Children Across Domains: A Reproducibility Study
• A Multistakeholder Approach to Value-Driven Co-Design of Recommender System Evaluation Metrics in Digital Archives
• Extending MovieLens-32M to Provide New Evaluation Objectives
• Accurate and Diverse Recommendations via Propensity-Weighted Linear Autoencoders
• On Inherited Popularity Bias in Cold-Start Item Recommendation