Long-Tail Session-based Recommendation from Calibration

Abstract

Accurate predictions in session-based recommendations have progressed, but a few studies have focused on skewed recommendation lists caused by popularity bias. Existing models for mitigating popularity bias have attempted to reduce the overconcentration of popular items by amplifying scores of less popular items. However, they normally ignore the users' different preferences toward long-tail items. Thus, we incorporate calibration, where calibrated recommendations reflect the users' interests in recommendation lists with appropriate proportions, to mitigate the popularity bias from the user's perspective. Specifically, we propose a calibration module to predict the ratio of tail items in the recommendation list from the session representation, and align it to the ongoing session. Additionally, we utilize a two-stage curriculum training strategy to improve prediction in the calibration module. Experiments on benchmark datasets show that our model can both achieve the competitive accuracy of recommendation and provide more tail items.

References in corpus (6)

• Neural Graph Collaborative Filtering
• Session-based Recommendation with Graph Neural Networks
• Recurrent Neural Networks with Top-k Gains for Session-based Recommendations
• Global Context Enhanced Graph Neural Networks for Session-based Recommendation
• Exploiting Cross-Session Information for Session-based Recommendation with Graph Neural Networks
• User-centered Evaluation of Popularity Bias in Recommender Systems

Cited by in corpus (1)

• A Comparative Analysis of Bias Amplification in Graph Neural Network Approaches for Recommender Systems