IBM computer usability satisfaction questionnaires: psychometric evaluation and instructions for use
International Journal of Human-Computer Interaction
Personalization of user profiles for content-based music retrieval based on relevance feedback
MULTIMEDIA '03 Proceedings of the eleventh ACM international conference on Multimedia
MusicSense: contextual music recommendation using emotional allocation modeling
Proceedings of the 15th international conference on Multimedia
Evaluating similarity measures for emergent semantics of social tagging
Proceedings of the 18th international conference on World wide web
On social networks and collaborative recommendation
Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval
Context awareness by case-based reasoning in a music recommendation system
UCS'07 Proceedings of the 4th international conference on Ubiquitous computing systems
Location-adapted music recommendation using tags
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
A mobile 3D-GIS hybrid recommender system for tourism
Information Sciences: an International Journal
Enhancing tag-based collaborative filtering via integrated social networking information
Proceedings of the 2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining
Hi-index | 0.00 |
Context-aware music recommender systems suggest music items taking into consideration contextual conditions, such as the user mood or location, that may influence the user preferences at a particular moment. In this paper we consider a particular kind of context-aware recommendation task: selecting music suited for a place of interest (POI), which the user is visiting, and that is illustrated in a mobile travel guide. We have designed an approach for this novel recommendation task by matching music to POIs using emotional tags. In order to test our approach, we have developed a mobile application that suggests an itinerary and plays recommended music for each visited POI. The results of the study show that users judge the recommended music suited for the POIs, and the music is rated higher when it is played in this usage scenario.