Improvement of similarity-diversity trade-off in recommender systems based on a facility location model

Abstract

There is a growing interest in the offering of novel alternative choices to users of recommender systems. These recommendations should match the target query while at the same time they should be diverse with each other in order to provide useful alternatives to the user, i.e. novel recommendations. In this paper, the problem of extracting novel recommendations, under the similarity-diversity trade-off, is modeled as a facility location problem. We formulate this trade-off as a multiple p-median problem solved by using biclustering. The results from tests in the benchmark Travel Case Base were satisfactory when compared to well-known recommender techniques, in terms of both similarity and diversity. Moreover, the experimental tests have shown that the proposed method is flexible enough, since a parameter of the adopted facility location model constitutes a regulator for the trade-off between similarity and diversity.