Womrad
Motivation
In the last decade, digital music has transformed the landscape of music experience and distribution. Personal music collections can exceed thousands of tracks, while the Internet has made it simpler than ever to find and access music. In this scenario, music recommendation systems have become increasingly important for listeners to discover and navigate music. Music-centric recommenders such as Last.fm and Pandora have enjoyed commercial and critical success. But how well do these systems work? How good are the recommendations? How far into the "long tail" can they go before surrendering to bad quality works?
The approach of recommending songs as if they were books is limiting; better results can be achieved by taking into account the peculiarities of music and the music recommendation process. A successful music recommender should combine insights from user preferences (classical collaborative filtering) with the content (audio analysis, tags, lyrics, etc..) while integrating the social interactions along with the psychological and emotional aspects connected to music consumption. The Workshop on Music Recommendation and Discovery is meant to be a platform where the Recommender System, Music Information Retrieval, User Modeling, Music Cognition, and Music Psychology communities can meet, exchange ideas and collaborate.
Topics of interest
- Music recommendation algorithms
- Theoretical aspects of music recommender systems
- User modeling in music recommender systems
- Similarity measures, and how to combine them
- Novel paradigms of music recommender systems
- Social tagging in music recommendation and discovery
- Social networks in music recommender systems
- Novelty, familiarity and serendipity in music recommendation and discovery
- Exploration and discovery in large music collections
- Evaluation of music recommender systems
- Evaluation of different sources of data/APIs for music recommendation and exploration
- Context-aware, mobile, and geolocation in music recommendation and discovery
- Case studies of music recommender system implementations
- User studies
- Innovative music recommendation applications
- Interfaces for music recommendation and discovery systems
- Scalability issues and solutions
- Semantic Web, Linking Open Data and Open Web Services for music recommendation and discovery
Organizers
2010
- Amélie Anglade - Centre for Digital Music, Queen Mary University of London, UK
- Claudio Baccigalupo - Ph.D. at the Artificial Intelligence Research Institute, IIIA-CSIC, Barcelona, Spain
- Norman Casagrande - last.fm, London, UK
- Òscar Celma - BMAT, Barcelona, Spain
- Paul Lamere - The Echo Nest, Somerville, MA, US
2011
- Amélie Anglade - Centre for Digital Music, Queen Mary University of London, UK
- Òscar Celma - BMAT, Barcelona, Spain
- Ben Fields - Musicmetric, London, UK
- Paul Lamere - The Echo Nest, Somerville, MA, US
- Brian McFee - UCSD, San Diego, US
