Information filtering based on user behavior analysis and best match text retrieval
SIGIR '94 Proceedings of the 17th annual international ACM SIGIR conference on Research and development in information retrieval
Proceedings of the 6th international conference on Intelligent user interfaces
Learning implicit user interest hierarchy for context in personalization
Proceedings of the 8th international conference on Intelligent user interfaces
Implicit feedback for inferring user preference: a bibliography
ACM SIGIR Forum
Accurately interpreting clickthrough data as implicit feedback
Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval
Personalizing search via automated analysis of interests and activities
Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval
Adaptive radio: achieving consensus using negative preferences
GROUP '05 Proceedings of the 2005 international ACM SIGGROUP conference on Supporting group work
ICAS '07 Proceedings of the Third International Conference on Autonomic and Autonomous Systems
Trust and nuanced profile similarity in online social networks
ACM Transactions on the Web (TWEB)
User profiles for personalized information access
The adaptive web
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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Recommender systems have explored a range of implicit feedback approaches to capture users' current interests and preferences without intervention of users' work. However, current research focuses mostly on implicit positive feedback. Implicit negative feedback is still a challenge because users mainly target information they want. There have been few studies assessing the value of negative implicit feedback. In this paper, we explore a specific approach to employ implicit negative feedback and assess whether it can be used to improve recommendation quality.