Information processing in dynamical systems: foundations of harmony theory
Parallel distributed processing: explorations in the microstructure of cognition, vol. 1
Rank aggregation methods for the Web
Proceedings of the 10th international conference on World Wide Web
Lifetrak: music in tune with your life
Proceedings of the 1st ACM international workshop on Human-centered multimedia
Group recommendations with rank aggregation and collaborative filtering
Proceedings of the fourth ACM conference on Recommender systems
Foxtrot: a soundtrack for where you are
Proceedings of Interacting with Sound Workshop: Exploring Context-Aware, Local and Social Audio Applications
ACM Transactions on Multimedia Computing, Communications, and Applications (TOMCCAP) - Special section on ACM multimedia 2010 best paper candidates, and issue on social media
Time Series Models for Semantic Music Annotation
IEEE Transactions on Audio, Speech, and Language Processing
Knowledge-based music retrieval for places of interest
Proceedings of the second international ACM workshop on Music information retrieval with user-centered and multimodal strategies
Proceedings of International Conference on Advances in Mobile Computing & Multimedia
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We propose a novel approach to context-aware music recommendation - recommending music suited for places of interest (POIs). The suggested hybrid approach combines two techniques -- one based on representing both POIs and music with tags, and the other based on the knowledge of the semantic relations between the two types of items. We show that our approach can be scaled up using a novel music auto-tagging technique and we compare it in a live user study to: two non-hybrid solutions, either based on tags or on semantic relations; and to a context-free but personalized recommendation approach. In the considered scenario, i.e., a situation defined by a context (the POI), we show that personalization (via music preference) is not sufficient and it is important to implement effective adaptation techniques to the user's context. In fact, we show that the users are more satisfied with the recommendations generated by combining the tag-based and knowledge-based context adaptation techniques, which exploit orthogonal types of relations between places and music tracks.