Explicit Elimination of Similarity Blocking for Session-based Recommendation

A single ‘odd’ interaction can cause two user interaction sessions to diverge in similarity, and stand in the way of generalization. The sensitivity of session-based recommenders to session similarity motivates us to explicitly identify and remove such ‘similarity blockers’. Specifically, we leverage huge amounts of data, which allow us to identify blockers in the form of non-co-occurring items. Other blockers can be identified using content-based similarity. Our experiments reveal that explicitly eliminating relatively few blockers improves performance.