Using collaborative filtering to weave an information tapestry
Communications of the ACM - Special issue on information filtering
Proceedings of the 6th international conference on Intelligent user interfaces
Proceedings of the 24th annual international ACM SIGIR conference on Research and development in information retrieval
E-Commerce Recommendation Applications
Data Mining and Knowledge Discovery
Implicit feedback for inferring user preference: a bibliography
ACM SIGIR Forum
Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
Combining Various Methods of Automated User Decision and Preferences Modelling
MDAI '09 Proceedings of the 6th International Conference on Modeling Decisions for Artificial Intelligence
Pref Shop A Web Shop with User Preference Search Capabilities
WI-IAT '10 Proceedings of the 2010 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology - Volume 03
Estimating importance of implicit factors in e-commerce recommender systems
Proceedings of the 2nd International Conference on Web Intelligence, Mining and Semantics
User feedback and preferences mining
UMAP'12 Proceedings of the 20th international conference on User Modeling, Adaptation, and Personalization
Negative implicit feedback in e-commerce recommender systems
Proceedings of the 3rd International Conference on Web Intelligence, Mining and Semantics
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In this paper, we investigate the possibilities of interpreting user behaviour in order to learn his/her preferences. UP Comp, a PHP component enabling use of user preferences for recommendation, is described. UP Comp is a standalone component that can be integrated into any PHP web with only basic knowledge of PHP, HTML and SQL. The methods of user behaviour interpretation are evaluated on a real web shop with tourist trips using UP Comp.