Proceedings of the 6th international conference on Intelligent user interfaces
Amazon.com Recommendations: Item-to-Item Collaborative Filtering
IEEE Internet Computing
Display time as implicit feedback: understanding task effects
Proceedings of the 27th annual international ACM SIGIR conference on Research and development in information retrieval
IEEE Transactions on Knowledge and Data Engineering
Adaptive radio: achieving consensus using negative preferences
GROUP '05 Proceedings of the 2005 international ACM SIGGROUP conference on Supporting group work
Collaborative Filtering for Implicit Feedback Datasets
ICDM '08 Proceedings of the 2008 Eighth IEEE International Conference on Data Mining
Reinforcing Recommendation Using Implicit Negative Feedback
UMAP '09 Proceedings of the 17th International Conference on User Modeling, Adaptation, and Personalization: formerly UM and AH
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
Recommender systems: from algorithms to user experience
User Modeling and User-Adapted Interaction
Noble reinforcement in disjunctive aggregation operators
IEEE Transactions on Fuzzy Systems
Expert Systems with Applications: An International Journal
Estimating importance of implicit factors in e-commerce recommender systems
Proceedings of the 2nd International Conference on Web Intelligence, Mining and Semantics
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In this paper, we imagine the situation of a typical e-commerce portal employing personalized recommendation. Such website typically receives user feedback from their implicit behavior such as time on page, scrolling etc. The implicit feedback is generally understood as positive only, however we present several methods how to identify some of the implicit feedback as negative user preference, how to aggregate various feedback types together and how to recommend based on it. We have conducted several off-line experiments with real user data from travel agency website confirming that treating some implicit feedback as negative preference can significantly improve recommendation quality.