Collaborative Filtering Recommendation Model Through Active Bayesian Classifier
Recommendation system recommends suitable products to customer through acquiring customers 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 samples 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.
GAO Lin-qi LI Cong-dong
Management School of Tianjin Normal University, Tianjin 300384, China;Management School of Tianjin U Management School of Tianjin University, Tianjin 300072, China
国际会议
2006 IEEE International Conference on Information Acquisition
山东威海
英文
572-577
2006-08-20（万方平台首次上网日期，不代表论文的发表时间）