Exploiting non-content preference attributes through hybrid recommendation method

This paper explores a method for incorporating into a recommender system explicit representations of user's preferences over non-content attributes such as popularity, recency, and similarity of recommended items. We show how such attributes can be modeled as a preference vector that can be used in a vector-space content-based recommender, and how that content-based recommender can be integrated with various collaborative f ltering techniques through reweighting of Top-M recommendations. We evaluate this approach on several recommender systems datasets and collaborative f ltering methods, and f nd that incorporating the three preference attributes can lead to a substantial increase in Top-50 precision while also enhancing diversity and novelty.