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User-item rating data preprocessing is an important factor that influences the accuracy of the collaborative filtering algorithms. When users assign a rating to an item, the rating may be influenced by some external factors, such as users' emotional factor. By analyzing the deviation of the users' ratings, this paper presents a novel recommendation method(More)
This paper presents a novel context-sensitive ranking algorithm, called ActiveRec, for providing flexible movie recommendations. Typically, ActiveRec can recommend movies to a user according to a specific movie type, or to a group of users satisfying their common interests. Firstly, ActiveRec constructs a multipartite graph where the nodes represent users,(More)
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