Using collaborative filtering to weave an information tapestry
Communications of the ACM - Special issue on information filtering
GroupLens: an open architecture for collaborative filtering of netnews
CSCW '94 Proceedings of the 1994 ACM conference on Computer supported cooperative work
Social information filtering: algorithms for automating “word of mouth”
CHI '95 Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Communications of the ACM
Authoritative sources in a hyperlinked environment
Journal of the ACM (JACM)
Proceedings of the 11th international conference on World Wide Web
E-Commerce Recommendation Applications
Data Mining and Knowledge Discovery
Extrapolation methods for accelerating PageRank computations
WWW '03 Proceedings of the 12th international conference on World Wide Web
Adaptive on-line page importance computation
WWW '03 Proceedings of the 12th international conference on World Wide Web
Computing pagerank in a distributed internet search system
VLDB '04 Proceedings of the Thirtieth international conference on Very large data bases - Volume 30
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Recommender systems improve access to relevant products and information by making suggestions based on page ranking technology. Existing approaches to learning to rank, however, did not consider the pages in the deep web which have valuable information. In this paper, we present a novel product recommendation algorithm based on the content of web pages including the product information and customer reviews. Our algorithm uses the customer reviews to calculate the score of dynamic web pages. The paper further focus on classifying the semantic orientation of the customer reviews through a progressed Bayesian Classifier and calculating the support value of each review. In addition, we also analyze the change tendency of customer reviews based on the temporal dimension. Experimental results shows that this approach can produce accurate recommendations.