Implicit feedback for inferring user preference: a bibliography
ACM SIGIR Forum
Generating semantically enriched user profiles for Web personalization
ACM Transactions on Internet Technology (TOIT)
Collaborative Filtering for Implicit Feedback Datasets
ICDM '08 Proceedings of the 2008 Eighth IEEE International Conference on Data Mining
I Like It... I Like It Not: Evaluating User Ratings Noise in Recommender Systems
UMAP '09 Proceedings of the 17th International Conference on User Modeling, Adaptation, and Personalization: formerly UM and AH
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)
Characterisation of explicit feedback in an online music recommendation service
Proceedings of the fourth ACM conference on Recommender systems
UMAP'11 Proceedings of the 19th international conference on User modeling, adaption, and personalization
Proceedings of the 2nd International Workshop on Information Heterogeneity and Fusion in Recommender Systems
Implicit feedback techniques on recommender systems applied to electronic books
Computers in Human Behavior
Estimating importance of implicit factors in e-commerce recommender systems
Proceedings of the 2nd International Conference on Web Intelligence, Mining and Semantics
User feedback and preferences mining
UMAP'12 Proceedings of the 20th international conference on User Modeling, Adaptation, and Personalization
Information Processing and Management: an International Journal
Negative implicit feedback in e-commerce recommender systems
Proceedings of the 3rd International Conference on Web Intelligence, Mining and Semantics
Silence is also evidence: interpreting dwell time for recommendation from psychological perspective
Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining
Generation of web recommendations using implicit user feedback and normalised mutual information
International Journal of Knowledge and Web Intelligence
Journal of Systems and Software
Automatic preference learning on numeric and multi-valued categorical attributes
Knowledge-Based Systems
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Explicit and implicit feedback exhibits different characteristics of users' preferences with both pros and cons. However, a combination of these two types of feedback provides another paradigm for recommender systems (RS). Their combination in a user preference model presents a number of challenges but can also overcome the problems associated with each other. In order to build an effective RS on combination of both types of feedback, we need to have comparative data allowing an understanding of the computation of user preferences. In this paper, we provide an overview of the differentiating characteristics of explicit and implicit feedback using datasets mined from Last.fm, an online music station and recommender service. The datasets consisted of explicit positive feedback (by loving tracks) and implicit feedback which is inherently positive (the number of times a track is played). Rather than relying on just one type of feedback, we present techniques for extracting user preferences from both. In order to compare and contrast the performances of these techniques, we carried out experiments using the Taste recommender system engine and the Last.fm datasets. Our results show that implicit and explicit positive feedback complements each other, with similar performances despite their different characteristics.