Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
GroupLens: an open architecture for collaborative filtering of netnews
CSCW '94 Proceedings of the 1994 ACM conference on Computer supported cooperative work
Analysis of recommendation algorithms for e-commerce
Proceedings of the 2nd ACM conference on Electronic commerce
Information Retrieval
Data Mining Techniques: For Marketing, Sales, and Customer Support
Data Mining Techniques: For Marketing, Sales, and Customer Support
Personalization of Supermarket Product Recommendations
Data Mining and Knowledge Discovery
Efficient Adaptive-Support Association Rule Mining for Recommender Systems
Data Mining and Knowledge Discovery
Mining Multiple-Level Association Rules in Large Databases
IEEE Transactions on Knowledge and Data Engineering
Learning Collaborative Information Filters
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
Prediction of members' return visit rates using a time factor
Electronic Commerce Research and Applications
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This paper proposes a recommendation methodology to help customers find the products they would like to purchase in a Web retailer. The methodology is based on collaborative filtering, but to overcome the sparsity issue, we employ an implicit ratings approach based on Web usage mining. Furthermore to address the scalability issue, a dimension reduction technique based on product taxonomy together with association rule mining is used. The methodology is experimentally evaluated on real Web retailer data and the results are compared to those of typical collaborative filtering. Experimental results show that our methodology provides higher quality recommendations and better performance, so it could be a promising marketer assistant tool for the Web retailer.