Abstract
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.
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Lee, D.H., Brusilovsky, P. (2009). Reinforcing Recommendation Using Implicit Negative Feedback. In: Houben, GJ., McCalla, G., Pianesi, F., Zancanaro, M. (eds) User Modeling, Adaptation, and Personalization. UMAP 2009. Lecture Notes in Computer Science, vol 5535. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-02247-0_47
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DOI: https://doi.org/10.1007/978-3-642-02247-0_47
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