PRUCARS: improved association rule-based social recommender systems using overlapping community detection

Abstract

Abstract With the increase of information on the web and online users’ activities, to find the appropriate information at the right time and discover items that customers are interested in among the available choices become difficult challenges. Recommender Systems have been introduced to overcome this problem by offering potentially relevant elements and providing users with personalized recommendations. Although several social-based recommendation techniques have been proposed in the literature, applying the community detection techniques based on previous transactions of users can improve the precision of the algorithms and better handle the data sparsity and cold-start problem. In this paper, we propose an improved association rule mining process based on Rule Power Factor to generate potent rules and enhance the accuracy of recommendation. The performance of the algorithm is analyzed on transactional datasets of MovieLens. The experimental results show that our method outperforms several state-of-the-art recommendation methods with increased precision, accuracy and minimum Mean Average Error values.