Exploiting the evaluation frequency of the items to enhance the recommendation accuracy

The main task of a recommender system is to suggest a list of items that users may be interested in. In this paper, we focus on the role that the popularity of the items plays in the recommendation process. If on the one hand, considering only the most popular items generates trivial recommendations, on the other hand, not taking in consideration the item popularity could lead to a non-optimal performance of a system, since it does not differentiate the items, giving them the same weight during the recommendation process. Therefore, we could risk to exclude from the recommendations some popular items that would have a high probability of being preferred by the users, suggesting instead others that, despite meeting the selection criteria, have less chance to be preferred. The proposed strategy aims to employ in the recommendation process new criteria based on the items' popularity, by introducing two novel metrics. Through the first metric we evaluate the semantic relevance of an item with respect to the user profile, while through the second metric, we measure how much it is preferred by users. Through a post-processing approach, we use these metrics in order to extend one of the most performing state-of-the-art recommendation techniques: SVD++. The effectiveness of this hybrid recommendation strategy has been verified through a series of experiments, which show strong improvements in terms of accuracy w.r.t. SVD++.