Implicit feedback for inferring user preference: a bibliography
ACM SIGIR Forum
Detecting noise in recommender system databases
Proceedings of the 11th international conference on Intelligent user interfaces
Generating semantically enriched user profiles for Web personalization
ACM Transactions on Internet Technology (TOIT)
Rate it again: increasing recommendation accuracy by user re-rating
Proceedings of the third ACM conference on Recommender systems
ACM Transactions on Computer-Human Interaction (TOCHI)
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
UMAP'11 Proceedings of the 19th international conference on User modeling, adaption, and personalization
Context-aware music recommender systems: workshop keynote abstract
Proceedings of the 21st international conference companion on World Wide Web
Optimal radio channel recommendations with explicit and implicit feedback
Proceedings of the sixth ACM conference on Recommender systems
Semi-automatic generation of recommendation processes and their GUIs
Proceedings of the 2013 international conference on Intelligent user interfaces
Generation of web recommendations using implicit user feedback and normalised mutual information
International Journal of Knowledge and Web Intelligence
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In this paper, we present our study and characterisation of explicit and implicit feedback on Last.fm, an online music station and recommender service. The dataset consisted of explicit positive feedback (through loved tracks) and implicit positive feedback (the number of times a track is played). As one would expect, our analysis shows that explicit feedback is very scarce. However, we also found that the rate at which a user provides explicit feedback decreases with time, and that overall leaving explicit feedback has a negative effect on the user's behaviour.