A new algorithm based on bipartite graph networks for improving aggregate recommendation diversity

Most of the traditional recommendation algorithms focus on the accuracy of recommendation results; however, the diversity of recommendation results is also important, which can be used to avoid the long-tail phenomenon. In this paper, a new algorithm for improving aggregate recommendation diversity is proposed. Firstly, a candidate recommendation list based on predictive scores is constructed; and then a bipartite graph network model is constructed. Secondly, item capacity is set to limit the number of recommendations of popular items. Finally, the final recommendation result is generated by combining the recommendation augmenting path. Based on the real world movie rating datasets, experiment results show that the proposed algorithm can effectively guarantee the accuracy of the recommendation results as well as improved the aggregate diversity of the recommendation.