Pointing the way: active collaborative filtering
CHI '95 Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Social information filtering: algorithms for automating “word of mouth”
CHI '95 Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
A vector space model for automatic indexing
Communications of the ACM
Item-based collaborative filtering recommendation algorithms
Proceedings of the 10th international conference on World Wide Web
Information Filtering: Overview of Issues, Research and Systems
User Modeling and User-Adapted Interaction
Is seeing believing?: how recommender system interfaces affect users' opinions
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Correlation-based Feature Selection for Discrete and Numeric Class Machine Learning
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Personalization of user profiles for content-based music retrieval based on relevance feedback
MULTIMEDIA '03 Proceedings of the eleventh ACM international conference on Multimedia
Experimenting with music taste prediction by user profiling
Proceedings of the 6th ACM SIGMM international workshop on Multimedia information retrieval
Automatic generation of personalized human avatars from multi-view video
Proceedings of the ACM symposium on Virtual reality software and technology
A probabilistic music recommender considering user opinions and audio features
Information Processing and Management: an International Journal - Special issue: AIRS2005: Information retrieval research in Asia
Music organisation using colour synaesthesia
CHI '07 Extended Abstracts on Human Factors in Computing Systems
A model-based approach to constructing music similarity functions
EURASIP Journal on Applied Signal Processing
Processing: A Programming Handbook for Visual Designers and Artists
Processing: A Programming Handbook for Visual Designers and Artists
Effective explanations of recommendations: user-centered design
Proceedings of the 2007 ACM conference on Recommender systems
The Benefit of Using Tag-Based Profiles
LA-WEB '07 Proceedings of the 2007 Latin American Web Conference
User Modeling and User-Adapted Interaction
A new approach to evaluating novel recommendations
Proceedings of the 2008 ACM conference on Recommender systems
The effects of transparency on trust in and acceptance of a content-based art recommender
User Modeling and User-Adapted Interaction
FOAFing the music: Bridging the semantic gap in music recommendation
Web Semantics: Science, Services and Agents on the World Wide Web
A novel method for personalized music recommendation
Expert Systems with Applications: An International Journal
Music search engines: Specifications and challenges
Information Processing and Management: an International Journal
Music Mood Annotator Design and Integration
CBMI '09 Proceedings of the 2009 Seventh International Workshop on Content-Based Multimedia Indexing
Music-driven character animation
ACM Transactions on Multimedia Computing, Communications, and Applications (TOMCCAP)
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
From Low-Level to High-Level: Comparative Study of Music Similarity Measures
ISM '09 Proceedings of the 2009 11th IEEE International Symposium on Multimedia
A novel music recommender by discovering preferable perceptual-patterns from music pieces
Proceedings of the 2010 ACM Symposium on Applied Computing
The Musical Avatar: a visualization of musical preferences by means of audio content description
Proceedings of the 5th Audio Mostly Conference: A Conference on Interaction with Sound
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
LIBSVM: A library for support vector machines
ACM Transactions on Intelligent Systems and Technology (TIST)
A music information system automatically generated via Web content mining techniques
Information Processing and Management: an International Journal
A probabilistic model for music recommendation considering audio features
AIRS'05 Proceedings of the Second Asia conference on Asia Information Retrieval Technology
Unifying Low-Level and High-Level Music Similarity Measures
IEEE Transactions on Multimedia
ESSENTIA: an open-source library for sound and music analysis
Proceedings of the 21st ACM international conference on Multimedia
Multimedia information retrieval: music and audio
Proceedings of the 21st ACM international conference on Multimedia
Hybrid recommendation approaches for multi-criteria collaborative filtering
Expert Systems with Applications: An International Journal
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Preference elicitation is a challenging fundamental problem when designing recommender systems. In the present work we propose a content-based technique to automatically generate a semantic representation of the user's musical preferences directly from audio. Starting from an explicit set of music tracks provided by the user as evidence of his/her preferences, we infer high-level semantic descriptors for each track obtaining a user model. To prove the benefits of our proposal, we present two applications of our technique. In the first one, we consider three approaches to music recommendation, two of them based on a semantic music similarity measure, and one based on a semantic probabilistic model. In the second application, we address the visualization of the user's musical preferences by creating a humanoid cartoon-like character - the Musical Avatar - automatically inferred from the semantic representation. We conducted a preliminary evaluation of the proposed technique in the context of these applications with 12 subjects. The results are promising: the recommendations were positively evaluated and close to those coming from state-of-the-art metadata-based systems, and the subjects judged the generated visualizations to capture their core preferences. Finally, we highlight the advantages of the proposed semantic user model for enhancing the user interfaces of information filtering systems.