A Recommender System With IBA Similarity Measure

Abstract

Recommender systems help users to reduce the amount of time they spend to find the items they are interested in. One of the most successful approaches is collaborative filtering. The main feature of a recommender system is its ability to predict user’s interests by analyzing the behavior of this particular user and/or the behavior of other similar users to generate personalized recommendations. Identification of neighbor users who have had similar taste to the target user in the past is a crucial process for successful application of collaborative filtering. In this paper, we proposed a collaborative filtering method that uses interpolative Boolean algebra for calculation of similarity between users. In order to analyze the effectiveness of the proposed approach we used three common datasets: MovieLens 100K, MovieLens 1M, and CiaoDVD. We compared a collaborative filtering based on IBA similarity measure with two standard similarity measures: Pearson correlation and cosine-based coefficient. Even though statistical measures are traditionally used in recommender systems, proposed logic-based approach showed promising results on the tested datasets. A recommender system with IBA similarity measure outperformed the others in most cases.