Proceedings of the 6th international conference on Intelligent user interfaces
Reinforcing Recommendation Using Implicit Negative Feedback
UMAP '09 Proceedings of the 17th International Conference on User Modeling, Adaptation, and Personalization: formerly UM and AH
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
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
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In this paper, we present our vision and some initial experiments on how to anticipate significance, similarity or polarity of various types of (preferably implicit) user feedback and how to form individual user preference for recommendation. Throughout the corporate web, we can observe the same patterns or actions in user behavior (e.g. page-view, amount of scrolling, rating or purchasing). Recorded user behavior --- user feedback --- is often used as base for personalized recommendation, but the connection between the feedback and user preference is often unclear or noisy. Our goal is to analyze user behavior in order to understand its relation to the user preference. We report on some initial experiments on a real-world e-commerce application. We describe our new models and methods how to combine various feedback types and how to learn user preferences.