Proceedings of the 6th international conference on Intelligent user interfaces
Hybrid Recommender Systems: Survey and Experiments
User Modeling and User-Adapted Interaction
A Movie Recommendation System—An Application of Voting Theory in User Modeling
User Modeling and User-Adapted Interaction
Evaluating the accuracy of implicit feedback from clicks and query reformulations in Web search
ACM Transactions on Information Systems (TOIS)
Collaborative Filtering for Implicit Feedback Datasets
ICDM '08 Proceedings of the 2008 Eighth IEEE International Conference on Data Mining
Combining Various Methods of Automated User Decision and Preferences Modelling
MDAI '09 Proceedings of the 6th International Conference on Modeling Decisions for Artificial Intelligence
Comparison of implicit and explicit feedback from an online music recommendation service
Proceedings of the 1st International Workshop on Information Heterogeneity and Fusion in Recommender Systems
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
Recipe recommendation: accuracy and reasoning
UMAP'11 Proceedings of the 19th international conference on User modeling, adaption, and personalization
UPComp - A PHP Component for Recommendation Based on User Behaviour
WI-IAT '11 Proceedings of the 2011 IEEE/WIC/ACM International Conferences on Web Intelligence and Intelligent Agent Technology - Volume 03
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 discuss the importance of different types of implicit user feedback for creating useful recommendations on an e-commerce website. Each website user may provide us with many different types of implicit feedback and it is difficult to decide which one to use for recommendations. If our recommendation algorithm support using more implicit factors, we should also consider importance and "added value" of each factor. We have identified several widely used implicit factors and conducted real user online testing in order to compare their usefulness for recommending algorithms. We have also proposed some combinations of implicit factors and a test, to see if they improve recommendation performance in comparison with the single factor ones.