Recommendation based on co-similarity and spanning tree with minimum weight

Abstract

Recommender system is a system that helps users find interesting items. Actually, collaborative filtering technology is one of the most successful techniques in recommender system. In this article we propose a new approach based on the rating of the users that is similar to the active one. In the literature, we find a lot of approaches able to recommend items to the user. Aiming to offer a list of interesting items, we use a hybrid approach of collaborative filtering that performs better than others. Our collaborative filtering approach is based on the graph theory, so we use the dissimilarity matrix as a spanning tree with minimum weight based on Kruskal algorithm. We define a group of criteria that help to determine the best items to recommend without computing the rating prediction.