WOMRAD 2010 - 1st Workshop on Music Recommendation and Discovery

Motivation

"Is Music Recommendation Broken? If so, how can we fix it?"
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
Topics of interest for Womrad 2010 include (but are not limited to):
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
Submission