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CSCW '94 Proceedings of the 1994 ACM conference on Computer supported cooperative work
Characterizing browsing strategies in the World-Wide Web
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Social information filtering: algorithms for automating “word of mouth”
CHI '95 Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
An adaptive Web page recommendation service
AGENTS '97 Proceedings of the first international conference on Autonomous agents
Recommendation as classification: using social and content-based information in recommendation
AAAI '98/IAAI '98 Proceedings of the fifteenth national/tenth conference on Artificial intelligence/Innovative applications of artificial intelligence
A re-examination of text categorization methods
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Content-based book recommending using learning for text categorization
DL '00 Proceedings of the fifth ACM conference on Digital libraries
Personalization on the Net using Web mining: introduction
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
A study of thresholding strategies for text categorization
Proceedings of the 24th annual international ACM SIGIR conference on Research and development in information retrieval
Collaborative filtering with privacy via factor analysis
SIGIR '02 Proceedings of the 25th annual international ACM SIGIR conference on Research and development in information retrieval
E-Commerce Recommendation Applications
Data Mining and Knowledge Discovery
Creating Adaptive Web Sites Through Usage-Based Clustering of URLs
KDEX '99 Proceedings of the 1999 Workshop on Knowledge and Data Engineering Exchange
A machine learning approach to web personalization
A machine learning approach to web personalization
Hybrid Recommendation Approaches: Collaborative Filtering via Valuable Content Information
HICSS '05 Proceedings of the Proceedings of the 38th Annual Hawaii International Conference on System Sciences - Volume 08
Automated user modeling for personalized digital libraries
International Journal of Information Management: The Journal for Information Professionals
Designing evolving user profile in e-CRM with dynamic clustering of Web documents
Data & Knowledge Engineering
An efficient hierarchical clustering model for grouping web transactions
International Journal of Business Intelligence and Data Mining
Retail sales prediction and item recommendations using customer demographics at store level
ACM SIGKDD Explorations Newsletter
Recommendation system based on the clustering of frequent sets
WSEAS Transactions on Information Science and Applications
Customer churn prediction by hybrid neural networks
Expert Systems with Applications: An International Journal
Review: Personalizing recommendations for tourists
Telematics and Informatics
HCII'11 Proceedings of the 14th international conference on Human-computer interaction: towards mobile and intelligent interaction environments - Volume Part III
Using lexicometry and vocabulary analysis techniques to detect a signature for web profile
Proceedings of the 2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining
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Practically every major company with a retail operation has its own web site and online sales facilities. This paper describes a toolset that exploits web usage data mining techniques to identify customer Internet browsing patterns. These patterns are then used to underpin a personalised product recommendation system for online sales. Within the architecture, a Kohonen neural network or self-organizing map (SOM) has been trained for use both offline, to discover user group profiles, and in real-time to examine active user click stream data, make a match to a specific user group, and recommend a unique set of product browsing options appropriate to an individual user. Our work demonstrates that this approach can overcome the scalability problem that is common among these types of system. Our results also show that a personalised recommender system powered by the SOM predictive model is able to produce consistent recommendations.