Collaborative Filtering Recommendation Model Through Active Bayesian Classifier

Recommendation system recommends suitable products to customer through acquiring customer´s requirement. The customers´ classifying becomes the basis to produce recommendation. Customers´ classifying has several features, such as huge sample space and class frequently changed. Traditional collaborative filtering algorithm works poor in this situation. To improve recommending quantity, a collaborative filtering model was proposed based on active Bayesian classifier. It has following features: (1) Through estimating sample´s utility for classifier, a sample selecting strategy was defined to reduce the number of samples while maintaining the quality of classification. (2) The training process of classifier is a loop procedure about sampling, label and study process. The termination condition of loop may be the time restraint, suits to the on-line application to increase recommendation speed. At last, experiments ware designed at the basis of MoveLens dataset. Comparing with general collaborative filtering, the proposed algorithm has higher quality of recommendation.