Proposing a novel method for improving the performance of collaborative filtering systems regarding the priority of similar users

Abstract

Recommender systems (RS) are an efficient and useful tool for personalizing services and providing efficient recommendations to users in different applications. One of the most successful techniques used in Recommender Systems is Collaborative Filtering (CF) which uses the rating matrix to find users with similar interests as the active user. The issue which usually arises with this method is the sparsity of the rating matrix which affects the process of finding similar users and the quality of the recommendations greatly. In this paper, a new method has been provided to increase the efficiency of the system against sparse data domains. The basis of the proposed method is extracting preference patterns from the rating matrix in the way that for each active user, a three-level tree of neighboring users is constructed. The active user is situated in the root of the tree, direct neighbors of the active user in the second level and indirect neighbors of the active user are situated in the third level. Then the similarity level of the active user with its direct and indirect neighbors is calculated. Finally the calculated similarity value is used in the process of predicting the ratings. This factor affects the quality of the ratings given by the neighbors. Results of the experiments on Movielens and Jester datasets indicate that in most cases, the proposed method provides better results than other widely utilized methods.