A New Collaborative Filtering Method using Representative Attributes-Neighborhood and Bayesian Estimated Value

초록

The user predicting preference method using a collaborative filtering(CF) doesn't only reflect any contents about item but also solve the problem of the Sparsity and first-Rater. In this paper, we suggest the method for predicting using Bayesian estimated value and the associative user lustering for the recalculation of preference to complement problems of the existing CF method. We suggest Representive Attributes - Neighborhood(RA-Neighborhood) method using for prediction finding the similar neighborhood through extracting the representative attributes that most affects the preference. We improved the efficiency by the associative user clustering analysis for recalculating the preference of specific item within the cluster item vector to the CF algorithm. Associative users are clustered according to the genre through using ARHP algorithm, and new users are classified into one of these genres by Naive Bayes classifier. Besides, to get the similarity between users belonged to the classified genre and new usersm this paper allows the differnt estimated value to the item which users evaluated through Naive Bayes learning. We evaluate our method on a large CF database of user rating and it significantly outperforms previous proposed method.