Diversity in a semantic recommender system

Abstract

In this paper, we introduce the notion of diversity in the recommender systems (RS). The aim is to provide the user with not only all the most relevant contents, but also the most diversified. To do this, we have developed a diversification algorithm that we have implemented on a semantic RS. This last performs the matching between the description of the contents and the user profile. A comparison of our algorithm to the diversity algorithm Swap, in terms of relevance and diversity, has revealed better results.