A User-Centric Diversity by Design Recommender System for the Movie Application Domain

Abstract

Recommender systems (RS) have seen widespread adoption across the Internet. However, by emphasizing personalization through the optimization of accuracy-focused metrics, over-personalization may emerge, with negative e ects on the user experience. A countermea- sure to the problem is to diversify recommendations. In this paper, we present a solution that addresses the problem in the context of a movie application domain. The solution enhances diversity on four related dimensions, namely global coverage, local coverage, novelty, and redundancy. The proposed solution is designed to diversify users pro les, modeled on categorical preferences, within the same group in the recommendation ltering. We evaluate our approach on the Movielens dataset and show that our algorithm yields bet- ter results compared to random selection distant neighbors and performs comparably to one of the current state of the art solutions.Recommender systems (RS) have seen widespread adoption across the Internet. However, by emphasizing personalization through the optimization of accuracy-focused metrics, over-personalization may emerge, with negative effects on the user experience. A countermeasure to the problem is to diversify recommendations. In this paper, we present a solution that addresses the problem in the context of a movie application domain. The solution enhances diversity on four related dimensions, namely global coverage, local coverage, novelty, and redundancy. The proposed solution is designed to diversify users profiles, modeled on categorical preferences, within the same group in the recommendation filtering. We evaluate our approach on the Movielens dataset and show that our algorithm yields better results compared to random selection distant neighbors and performs comparably to one of the current state of the art solutions