NDLI: Collaborative Filtering Recommendation Model Through Active Bayesian Classifier

Content Provider	IEEE Xplore Digital Library
Author	Gao Lin-qi Li Cong-dong
Copyright Year	2006
Description	Author affiliation: Manage. Sch., Tianjin Normal Univ., Tianjin (Gao Lin-qi)
Abstract	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.
Starting Page	572
Ending Page	577
File Size	122627
Page Count	6
File Format	PDF
ISBN	1424405289
DOI	10.1109/ICIA.2006.305776
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2006-08-20
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Machine learning algorithms Bayesian methods Collaboration Clustering algorithms Filtering algorithms Sampling methods Collaborative work Marketing and sales Electronic commerce Data mining
Content Type	Text
Resource Type	Article

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