Long-Tail Session-based Recommendation from Calibration
This work addresses skewed recommendation lists for users in session-based systems, offering an incremental improvement by focusing on user-specific calibration.
The paper tackled the problem of popularity bias in session-based recommendation by incorporating calibration to reflect users' preferences for long-tail items, resulting in competitive recommendation accuracy and increased inclusion of tail items.
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.