UIIM: A User-Item Interest Model Based on Bipartite Network for Top-N Recommender System

Abstract

Recently, a sparse linear method (SLIM) is developed for top-N recommender systems, which can produce high-quality recommendations for sparse data sets.SLIM provides a better performance than other existing methods.In this paper, we provide a novel user-item interest method (UIIM) based on bipartite network to improve the performance of SLIM.UIIM generates top-N recommendations by building the user-item interest matrix R with the bipartite network of users and items, calculating the item-item similarity matrix with SLIM and predicting users' ratings on items as a dot product of matrix and .And we also provide a parallel algorithm based on Spark to learn .Our results indicate that UIIM provides better performance and recommendation quality than other existing methods and parallel algorithm of learning outperforms serial algorithm on large-scale data sets.