GroupLens: an open architecture for collaborative filtering of netnews
CSCW '94 Proceedings of the 1994 ACM conference on Computer supported cooperative work
A learning agent for wireless news access
Proceedings of the 5th international conference on Intelligent user interfaces
Analysis of recommendation algorithms for e-commerce
Proceedings of the 2nd ACM conference on Electronic commerce
Item-based collaborative filtering recommendation algorithms
Proceedings of the 10th international conference on World Wide Web
IBERAMIA 2002 Proceedings of the 8th Ibero-American Conference on AI: Advances in Artificial Intelligence
PTV: Intelligent Personalised TV Guides
Proceedings of the Seventeenth National Conference on Artificial Intelligence and Twelfth Conference on Innovative Applications of Artificial Intelligence
An industrial-strength content-based music recommendation system
Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval
Feature weighting in content based recommendation system using social network analysis
Proceedings of the 17th international conference on World Wide Web
Mining recommendations from the web
Proceedings of the 2008 ACM conference on Recommender systems
An adaptive personalized news dissemination system
Journal of Intelligent Information Systems
Collaborative filtering recommender systems
The adaptive web
Creating collections with automatic suggestions and example-based refinement
UIST '10 Proceedings of the 23nd annual ACM symposium on User interface software and technology
Vintage radio interface: analog control for digital collections
CHI '12 Extended Abstracts on Human Factors in Computing Systems
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In this paper, we present a Web recommender system for recommending, predicting and personalizing music playlists based on a user model. We have developed a hybrid similarity matching method that combines collaborative filtering with ontology-based semantic distance measurements. We dynamically generate a personalized music playlist, from a selection of recommended playlists, which comprises the most relevant tracks to the user. Our Web recommender system features three functionalities: (1) predict the likability of a user towards a specific music playlist, (2) recommend a set of music playlists, and (3) compose a new personalized music playlist. Our experimental results will show the efficacy of our hybrid similarity matching approach and the information personalization method.