Exploring Popularity Bias in Session-based Recommendation
Haowen Wang
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.