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
PHOAKS: a system for sharing recommendations
Communications of the ACM
Fab: content-based, collaborative recommendation
Communications of the ACM
An adaptive Web page recommendation service
AGENTS '97 Proceedings of the first international conference on Autonomous agents
Analysis of recommendation algorithms for e-commerce
Proceedings of the 2nd ACM conference on Electronic commerce
Item-based collaborative filtering recommendation algorithms
Proceedings of the 10th international conference on World Wide Web
A Recommendation System for Software Function Discovery
APSEC '02 Proceedings of the Ninth Asia-Pacific Software Engineering Conference
Google news personalization: scalable online collaborative filtering
Proceedings of the 16th international conference on World Wide Web
Recommender systems and their impact on sales diversity
Proceedings of the 8th ACM conference on Electronic commerce
Collaborative Filtering Using Dual Information Sources
IEEE Intelligent Systems
New Recommendation Techniques for Multicriteria Rating Systems
IEEE Intelligent Systems
Attacks and Remedies in Collaborative Recommendation
IEEE Intelligent Systems
MarCol: A Market-Based Recommender System
IEEE Intelligent Systems
Modeling relationships at multiple scales to improve accuracy of large recommender systems
Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
Empirical analysis of predictive algorithms for collaborative filtering
UAI'98 Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence
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We propose herein a technique for product recommendation in E-commerce by considering the context of product purchases, and verify the effectiveness of the technique through an evaluation experiment. Researchers have been aggressively studying techniques that can be used by stores to recommend to customers products that have relatively high purchase potential. Collaborative filtering is representative of conventional techniques. However, the collaborative filtering technique is based on the hypothesis that similar customers purchase similar products, and the context of product purchases is not considered in full. In the present study, a context matrix by which to manage the context history of product purchases is proposed. Collaborative filtering cannot distinguish the following two facts that 'Product B was purchased after Product A' and 'Product A was purchased after Product B'. The context matrix, however, enables such information to be expressed and managed separately. We also propose four types of context matrix update methods which differs in subset selection on purchase history and user selection on obtaining purchase history. The results of an evaluation experiment revealed the following: i) The proposed technique can improve the recommendation precision by taking into account the context of purchases when making recommendations. ii) As the amount of available purchase history and context data increases, the recommendation precision improves. iii) The highest recommendation precision among four types of context matrix update methods is obtained, if all contexts of purchases along time axis by customers of similar taste only are considered.