Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
Automatic personalization based on Web usage mining
Communications of the ACM
Item-based collaborative filtering recommendation algorithms
Proceedings of the 10th international conference on World Wide Web
Data Mining Techniques: For Marketing, Sales, and Customer Support
Data Mining Techniques: For Marketing, Sales, and Customer Support
Hybrid Recommender Systems: Survey and Experiments
User Modeling and User-Adapted Interaction
Expert-Driven Validation of Rule-Based User Models in Personalization Applications
Data Mining and Knowledge Discovery
Mining Multiple-Level Association Rules in Large Databases
IEEE Transactions on Knowledge and Data Engineering
Mining customer product ratings for personalized marketing
Decision Support Systems - Special issue: Web data mining
Integrating AHP and data mining for product recommendation based on customer lifetime value
Information and Management
IEEE Transactions on Knowledge and Data Engineering
Journal of Systems and Software
WordNet-based User Profiles for Neighborhood Formation in Hybrid Recommender Systems
HIS '05 Proceedings of the Fifth International Conference on Hybrid Intelligent Systems
A new approach for combining content-based and collaborative filters
Journal of Intelligent Information Systems
A collaborative filtering framework based on fuzzy association rules and multiple-level similarity
Knowledge and Information Systems
A recommender system using GA K-means clustering in an online shopping market
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Modeling user multiple interests by an improved GCS approach
Expert Systems with Applications: An International Journal
A personalized recommendation system based on product taxonomy for one-to-one marketing online
Expert Systems with Applications: An International Journal
Feature-based recommendations for one-to-one marketing
Expert Systems with Applications: An International Journal
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Recommender systems are changing from novelties used by a few E-commerce sites to serious business tools. They recommend products to customers based on their historical preferences. Through several recommendation techniques, Collaborative filtering (CF) is the most successful recommendation method which is widely used. Nowadays, customer lifetime value (CLV) is measured by RFM (Recency, Frequency, and Monetary) and weighted RFM-based method is used in product recommendation. In this paper, we present a product recommendation technique for online retail stores which employs CLV concept and integrates it with CF method to generate better quality recommendations. In this paper, CF is applied to customer ratings on products, which are collected implicitly by web usage mining approach. Product taxonomy is also used to segment products according to their categories and to reduce dimensions of computational space. The experimental results show that the proposed technique outperforms several other similar recommendation methods.