Popularity Does Not Always Mean Triviality: Introduction of Popularity Criteria to Improve the Accuracy of a Recommender System
Roberto Saia, Ludovico Boratto, Salvatore Carta · Journal of Computers · 2017
The main goal of a recommender system is to provide suggestions, by predicting a set of items that might interest the users.In this paper, we will focus on the role that the popularity of the items can play in the recommendation process.The main idea behind this work is that if an item with a high predicted rating for a user is very popular, this information about its popularity can be effectively employed to select the items to recommend.Indeed, by merging a high predicted rating with a high popularity, the effectiveness of the produced recommendations would increase with respect to a case in which a less popular item is suggested.The proposed strategy aims to employ in the recommendation process new criteria based on the items' popularity, by measuring how much it is preferred by users.Through a post-processing approach, we use this metric to extend one of the most performing state-of-the-art recommendation techniques, i.e., SVD++.The effectiveness of this hybrid strategy of recommendation has been verified through a series of experiments, which show strong improvements in terms of accuracy w.r.t.SVD++.