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- W2351105341 abstract "Recommender system uses customers' historical preferences to realize personal service. It has become an important application in E-commerce and Information access. This paper presents a new recommendation method, which combines the similar relation in attributes and characters of items with Item-Based Collaborative Filtering recommendation technology by fuzzy clustering method. Firstly it applies fuzzy clustering method to cluster the items and gets the similar group in the attributes and characters of items. Then this paper forms the synthetical similar group by combining the similar group with the collaborative similar group in user-item rating matrix. At last this new method makes the recommendation for active customers based on the synthetical similar group. The experimental results show that this method can not only efficently improve the Cold-Start problem of Item-based Collaborative Filtering recommendation algorithm, but also provide better recommendation results than that of Item-based Collaborative Filtering recommendation algorithm." @default.
- W2351105341 created "2016-06-24" @default.
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- W2351105341 date "2005-01-01" @default.
- W2351105341 modified "2023-09-23" @default.
- W2351105341 title "A Collaborative Filtering Recommendation Method Based on Fuzzy Clustering" @default.
- W2351105341 hasPublicationYear "2005" @default.
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