Exploring Popularity Bias in Session-based Recommendation

• URL: http://arxiv.org/abs/2312.07855v1
• Date: Wed, 13 Dec 2023 02:48:35 GMT
• Title: Exploring Popularity Bias in Session-based Recommendation
• Authors: Haowen Wang
• Abstract summary: We extend the analysis to session-based setup and adapted propensity calculation to the unique characteristics of session-based recommendation tasks. We study the distributions of propensity and different stratification techniques on different datasets and find that propensity-related traits are actually dataset-specific.
• Score: 0.6798775532273751
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Existing work has revealed that large-scale offline evaluation of recommender systems for user-item interactions is prone to bias caused by the deployed system itself, as a form of closed loop feedback. Many adopt the \textit{propensity} concept to analyze or mitigate this empirical issue. In this work, we extend the analysis to session-based setup and adapted propensity calculation to the unique characteristics of session-based recommendation tasks. Our experiments incorporate neural models and KNN-based models, and cover both the music and the e-commerce domain. We study the distributions of propensity and different stratification techniques on different datasets and find that propensity-related traits are actually dataset-specific. We then leverage the effect of stratification and achieve promising results compared to the original models.

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