Improving collaborative filtering-based recommender systems results using Pareto dominance

Abstract

Recommender systems are a type of solution to the information overload problem suffered by users of websites that allow the rating of certain items. The collaborative filtering recommender system is considered to be the most successful approach, as it makes its recommendations based on ratings provided by users who are similar to the active user. Nevertheless, the traditional collaborative filtering method can select insufficiently representative users as neighbours of the active user. This means that recommendations made a posteriori are not sufficiently precise. The method proposed in this paper uses Pareto dominance to perform a pre-filtering process eliminating less representative users from the k-neighbour selection process while retaining the most promising ones. The results from experiments performed on the Movielens and Netflix websites show significant improvements in all tested quality measures when the proposed method is applied.