Collaborative filtering is a well-known technique successfully used in various recommender systems. However, it suffers from major drawbacks of the scalability and the data sparsity problems, when the system makes a recommendation based on the ratings records of similar users. This study aims at solving these problems by exploiting user interest in movie genres to build clusters of users. We make a slight variation of Fuzzy C-means algorithm such that several centers, one for each genre, per cluster are maintained. Experimental results showed that the proposed strategy demonstrated performance comparable to a conventional method without using clustering and significantly better than a well-known clustering algorithm of K-means.
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