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.
@article{arxiv.1209.1719,
title = {Semi-metric networks for recommender systems},
author = {Tiago Simas and Luis M. Rocha},
journal= {arXiv preprint arXiv:1209.1719},
year = {2012}
}