Coverage, redundancy and size-awareness in genre diversity for recommender systems
Saúl Vargas, Linas Baltrunas, Alexandros Karatzoglou, Pablo Castells · 2014
There is increasing awareness in the Recommender Systems field that diversity is a key property that enhances the usefulness of recommendations. Genre information can serve as a means to measure and enhance the diversity of recommendations and is readily available in domains such as movies, music or books. In this work we propose a new Binomial framework for defining genre diversity in recommender systems that takes into account three key properties: genre coverage, genre redundancy and recommendation list size-awareness. We show that methods previously proposed for measuring and enhancing recommendation diversity - including those adapted from search result diversification - fail to address adequately these three properties. We also propose an efficient greedy optimization technique to optimize Binomial diversity. Experiments with the Netflix dataset show the properties of our framework and comparison with state of the art methods.