Abstract

In E-commerce sites, recommendation systems are used torecommend products to their customers. Collaborative filtering (CF) iswidely employed approach to recommend products. In the literature,researchers are making efforts to improve the scalability and onlineperformance of CF. In this paper we propose a graph based approach toimprove the performance of CF. We abstract a neighborhood communityof a given customer through dense bipartite graph (DBG). Given adata set of customer preferences, a group of neighborhood customersfor a given customer is extracted by extracting corresponding DBG.The experimental results on the MovieLens data set show that therecommendation made with the proposed approach matches closelywith the recommendation of CF. The proposed approach possesses apotential to adopt to frequent changes in the product preference data set.