On Evaluating Session-Based Recommendation with Implicit Feedback

Session-based recommendation systems are used in environments where system recommendation actions are interleaved with user choice reactions. Domains include radio-style song recommendation, session-aware related-items in a shopping context, and next video recommendation. In many situations, interactions logged from a production policy can be used to train and evaluate such session-based recommendation systems. This paper presents several concerns with interpreting logged interactions as reflecting user preferences and provides possible mitigations to those concerns.