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
Recommending and evaluating choices in a virtual community of use
CHI '95 Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Social information filtering: algorithms for automating “word of mouth”
CHI '95 Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Fab: content-based, collaborative recommendation
Communications of the ACM
Learning and Revising User Profiles: The Identification ofInteresting Web Sites
Machine Learning - Special issue on multistrategy learning
Analysis of recommendation algorithms for e-commerce
Proceedings of the 2nd ACM conference on Electronic commerce
Introduction to Modern Information Retrieval
Introduction to Modern Information Retrieval
Data Mining: An Overview from a Database Perspective
IEEE Transactions on Knowledge and Data Engineering
Amazon.com Recommendations: Item-to-Item Collaborative Filtering
IEEE Internet Computing
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
PILGRIM: A Location Broker and Mobility-Aware Recommendation System
PERCOM '03 Proceedings of the First IEEE International Conference on Pervasive Computing and Communications
The attraction of personalized service for users in mobile commerce: an empirical study
ACM SIGecom Exchanges - Mobile commerce
Understanding usability in mobile commerce
Communications of the ACM - Mobile computing opportunities and challenges
VISCORS: A Visual-Content Recommender for the Mobile Web
IEEE Intelligent Systems
A review for mobile commerce research and applications
Decision Support Systems
Managing electronic commerce retail transaction costs for customer value
Decision Support Systems
Similarity Measure and Instance Selection for Collaborative Filtering
International Journal of Electronic Commerce
An overview of clustering methods
Intelligent Data Analysis
A multi-stage collaborative filtering approach for mobile recommendation
Proceedings of the 3rd International Conference on Ubiquitous Information Management and Communication
Handling sequential pattern decay: Developing a two-stage collaborative recommender system
Electronic Commerce Research and Applications
Mining changes in customer buying behavior for collaborative recommendations
Expert Systems with Applications: An International Journal
Adding clicks to bricks: A study of the consequences on customer loyalty in a service context
Electronic Commerce Research and Applications
Exploiting two-faceted web of trust for enhanced-quality recommendations
Expert Systems with Applications: An International Journal
Prediction of members' return visit rates using a time factor
Electronic Commerce Research and Applications
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The number of third generation (3G) subscribers conducting mobile commerce has increased as mobile data communications have evolved. Multi-channel companies that wish to develop mobile commerce face difficulties due to the lack of knowledge about users' consumption behavior on new mobile channels. Typical collaborative filtering (CF) recommendations may be affected by the so-called sparsity problem because relatively few products are browsed or purchased on the mobile Web. In this study, we propose a hybrid multiple channel method to address the lack of knowledge about users' consumption behavior on a new channel and the difficulty of finding similar users due to the sparsity problem of typical CF recommender systems. Products are recommended to users based on their browsing behavior on the new mobile channel as well as the consumption behavior of heavy users of existing channels, such as television, catalogs, and the Web. Our experiment results show that the proposed method performs well compared to the other recommendation methods.