Improving diffusion-based recommendation in online rating systems
Abstract
Network diffusion processes play an important role in solving the information overload problem. It has been shown that the diffusion-based recommendation methods have the advantage to generate both accurate and diverse recommendation items for online users. Despite that, numerous existing works consider the rating information as link weight or threshold to retain the useful links, few studies use the rating information to evaluate the recommendation results. In this paper, we measure the average rating of the recommended products, finding that diffusion-based recommendation methods have the risk of recommending low-rated products to users. In addition, we use the rating information to improve the network-based recommendation algorithms. The idea is to aggregate the diffusion results on multiple user-item bipartite networks each of which contains only links of certain ratings. By tuning the parameters, we find that the new method can sacrifice slightly the recommendation accuracy for improving the average rating of the recommended products.
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