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Collaborative filtering methods are utilized to provide personalized recommendations for users in order to alleviate information overload problem in different domains. Traditional collaborative filtering methods operate on a user-item matrix in which each user reveal her admiration about an item based on a single criterion. However, recent studies indicate that recommender systems depending on multi-criteria...
With the rapid development of Internet, Recommender Systems can help us efficiently find the useful objects in the information era. Generally, the traditional random walk algorithm has high accuracy but low personality and diversity. In this paper, we propose an improved random walk algorithm by depressing the influence of large-degree objects. Experimental results on MovieLens and Netflix data sets...
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