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    Abstract:

    Traditional recommendation system has the problem of sparse user ratings and system scalability. This paper proposes a recommendation system based on intelligence multi-agent. At first, the cosine similarity measure has been used to handle user-item rating matrix, thus the initial neighbor set for target users can be gained. Then, user ratings have been mapped to relevant item attributes for generating user-attributes value preference matrix UPm of each user. Thus, user similarity can be computed based on UPm and rating sparsity has been alleviated simultaneously. The recommendation system of intelligence multi-agent makes calculating an online processing, and thus improves the system scalability. Experimental results show that the new system achieves a better accuracy in recommended convergence.

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王卫平,赵明,刘迎意,王选.基于智能多agent的推荐系统.计算机系统应用,2010,19(2):1-5

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  • Received:May 19,2009
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