WOMRAD 2010 - 1st Workshop on Music Recommendation and Discovery

Website womrad.org/2010 | Edit Freely

Category WOMRAD 2010

Deadline: June 28, 2010 | Date: September 26, 2010

Venue/Country: Barcelona, Spain

Updated: 2010-06-04 19:32:22 (GMT+9)

Call For Papers - CFP

"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