An entropy-based neighbor selection approach for collaborative filtering
Knowledge-Based Systems
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Collaborative filtering has been very successful in both research and applications. In collaborative filtering algorithm, the most important process is selecting neighbors for the active user. Traditional methods compute user's similarity on the whole set of items. Because researchers believed if users have similar preference on some of items, they will have the similar preference on other items. But we argue that users have similar preference only on parts of items, not the whole items. For this reason, we try to analyze the problem of traditional approach in the process of selecting neighbors in our paper. And then we propose a novel method to select neighbors for the active user by using the variance of the process of computing similarity between users. Experimental results show that our approach can significantly improve the accuracy of predication.