GroupLens: an open architecture for collaborative filtering of netnews
CSCW '94 Proceedings of the 1994 ACM conference on Computer supported cooperative work
Recommender systems in e-commerce
Proceedings of the 1st ACM conference on Electronic commerce
A Framework for Collaborative, Content-Based and Demographic Filtering
Artificial Intelligence Review - Special issue on data mining on the Internet
Automatic personalization based on Web usage mining
Communications of the ACM
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
Web mining for web personalization
ACM Transactions on Internet Technology (TOIT)
An Automatic Rating Technique Based on XML Document
AH '02 Proceedings of the Second International Conference on Adaptive Hypermedia and Adaptive Web-Based Systems
Improving the Effectiveness of a Web Site with Web Usage Mining
WEBKDD '99 Revised Papers from the International Workshop on Web Usage Analysis and User Profiling
A Case-Based Recommender System Using Implicit Rating Techniques
AH '02 Proceedings of the Second International Conference on Adaptive Hypermedia and Adaptive Web-Based Systems
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We propose a new personalized recommendation technique, which dynamically recommends products based on user behavior patterns for E-commerce. It collects and analyzes user behavior patterns from XML-based E-commerce sites using the PRML (Personalized Recommendation Markup Language) approach. The collected information is saved as PRML instances and an individual user profile is built from the PRML instances of the user using a CBR (Case-Based Reasoning) learning technique. When a new product is introduced, the system compares, for a user, the preference information saved in the user profile and the information about the new product and produces a recommendation that best fits the user preference.