Collaborative filtering algorithm based on mutual information
作者： Wang Ziqiang , Feng Boqin
DOI: 10.1007/978-3-540-24655-8_43
关键词: Mutual information 、 Algorithm 、 Computer science 、 Collaborative filtering 、 Data mining 、 User assistance 、 Recommender system 、 Information overload 、 Information filtering system 、 Weighting 、 Feature (computer vision)
摘要: As information spaces such as the WWW grow ever larger, need for tools to help users find high quality reliable quickly and easily becomes more acute. Collaborative filtering (CF) based recommender systems have emerged in response these problems. is a popular technique reducing overload has seen considerable successes many area. In order further improve accuracy of filtering, new approach collaborative algorithms proposed using mutual information. The method on simultaneous feature weighting relevant instance selection.The methods are evaluated well-known EachMovie dataset experimental results demonstrate significant improvement efficiency.
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