Clustering-based recommender system using principles of voting theory

Recommender Systems (RS) are widely used for providing automatic personalized suggestions for information, products and services. Collaborative Filtering (CF) is one of the most popular recommendation techniques. However, with the rapid growth of the Web in terms of users and items, majority of the RS using CF technique suffer from problems like data sparsity and scalability. In this paper, we present a Recommender System based on data clustering techniques to deal with the scalability problem associated with the recommendation task. We use different voting systems as algorithms to combine opinions from multiple users for recommending items of interest to the new user. The proposed work use DBSCAN clustering algorithm for clustering the users, and then implement voting algorithms to recommend items to the user depending on the cluster into which it belongs. The idea is to partition the users of the RS using clustering algorithm and apply the Recommendation Algorithm separately to each partition. Our system recommends item to a user in a specific cluster only using the rating statistics of the other users of that cluster. This helps us to reduce the running time of the algorithm as we avoid computations over the entire data. Our objective is to improve the running time as well as maintain an acceptable recommendation quality. We have tested the algorithm on the Netflix prize dataset.