Semi-metric networks for recommender systems
This addresses recommendation accuracy for users in systems like Movielens, but appears incremental as it builds on existing graph-based methods.
The paper tackled the problem of non-metric topologies in weighted graphs from user-item co-occurrence by using semi-metric behavior for recommendations, showing that including highly semi-metric edges leads to better recommendations on the Movielens benchmark.
Weighted graphs obtained from co-occurrence in user-item relations lead to non-metric topologies. We use this semi-metric behavior to issue recommendations, and discuss its relationship to transitive closure on fuzzy graphs. Finally, we test the performance of this method against other item- and user-based recommender systems on the Movielens benchmark. We show that including highly semi-metric edges in our recommendation algorithms leads to better recommendations.