Fair recommendations through diversity promotion

Abstract

We address the problem of overspecialization in streaming platform recommender systems. The personalization of web pages by delivering content to users is a challenging task in data mining. But it has been proved that beside optimizing the relevance accuracy such systems should also rely on other factors like diversity or novelty. In this paper we focus on modeling users’ boundary area of interest by selecting the most diverse items they liked in the past. We apply diversification while building the top-N list of recommendations. We select the items we want to recommend from an area where we consider a user will find item different from what she or he likes in the past. We evaluate our approach in offline analysis on two datasets, showing that our approach brings diversity and is competitive against implicit state-of-the-art method.