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By analyzing the common sparse problems of the score data in the collaborative filtering recommendation system, this paper presents a recommendation method for mobile networks based on user characteristics and user trust relationship. In this method, the similarity of user characteristic attributes is used to modify the similarity calculating of the original score. Then, according to the communication...
Traditional recommendation algorithms face some serious problems, including data sparsity, cold start and inefficiency. To better address the problems above, the paper proposes a hybrid recommendation algorithm based on improved collaborative filtering of user context fuzzy clustering and content-based. For collaborative filtering, firstly, user classification is based on fuzzy clustering according...
Collaborative filtering is key technique of recommendation system. But traditional collaborative filtering methods are inefficient especially when the user-rating data is extremely sparse. To solve this problem, we propose an approach to compute the user similarity with the type of users-rating items in this paper, and then we develop a collaborative filtering algorithm based on this approach. Furthermore,...
Most recommendation systems employ collaborative filtering (CF) for formulating suggestions of items relevant to users' interests, which commonly uses k-nearest neighbors searching algorithm (kNN) and recommends an item to a user based on the users' rating table. With the users' number increasing, it meets the real-time problem and the scalable problem. In this paper, we propose the original AS-INDEX...
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