Womrad - Revision history

Ocelma: /* 2010 */

2011-06-10T03:39:04Z

2010

Ocelma: /* 2011 */

2011-06-10T03:38:49Z

2011

Ocelma: /* Organizers */ added 2011

2011-06-10T03:38:25Z

Organizers: added 2011

Zeno Gantner at 08:43, 7 June 2011

2011-06-07T08:43:02Z

Zeno Gantner: /* Topics of interest */

2011-06-07T08:39:54Z

Topics of interest

Zeno Gantner at 08:33, 7 June 2011

2011-06-07T08:33:41Z

92.231.112.190 at 23:50, 23 March 2011

2011-03-23T23:50:37Z

Ocelma: Created page with "http://womrad.org == Motivation == In the last decade, digital music has transformed the landscape of music experience and distribution. Personal music collections can exceed th..."

2011-03-23T22:35:32Z

Created page with "http://womrad.org == Motivation == In the last decade, digital music has transformed the landscape of music experience and distribution. Personal music collections can exceed th..."

New page

http://womrad.org

== 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 ==

* Amélie Anglade - Centre for Digital Music, Queen Mary University of London, UK (2010)
* Claudio Baccigalupo - Ph.D. at the Artificial Intelligence Research Institute, IIIA-CSIC, Barcelona, Spain (2010)
* Norman Casagrande - last.fm, London, UK (2010)
* Òscar Celma - BMAT, Barcelona, Spain (2010)
* Paul Lamere - The Echo Nest, Somerville, MA, US (2010)

@@ Line 33: / Line 33: @@
 * 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
+* Òscar Celma - [[BMAT]], Barcelona, Spain
 * Paul Lamere - [[The Echo Nest]], Somerville, MA, US

@@ Line 39: / Line 39: @@
 * Amélie Anglade - Centre for Digital Music, Queen Mary University of London, UK
-* Òscar Celma - BMAT, Barcelona, Spain
+* Òscar Celma - [[BMAT]], Barcelona, Spain
 * Ben Fields - Musicmetric, London, UK
 * Paul Lamere - [[The Echo Nest]], Somerville, MA, US

@@ Line 36: / Line 36: @@
 * http://womrad.org
-[[Category: Workshops]]
+[[Category: Workshop]]
 [[Category: Music recommendation]]

@@ Line 16: / Line 16: @@
 * Novelty, familiarity and [[serendipity]] in music recommendation and discovery
 * [[Exploration]] and discovery in large music collections
-* [[:Category:Evaluation]] of music recommender systems
+* [[:Category:Evaluation|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

← Older revision		Revision as of 23:50, 23 March 2011
Line 36:		Line 36:
	* Òscar Celma - BMAT, Barcelona, Spain (2010)		* Òscar Celma - BMAT, Barcelona, Spain (2010)
	* Paul Lamere - The Echo Nest, Somerville, MA, US (2010)		* Paul Lamere - The Echo Nest, Somerville, MA, US (2010)
		+	[[Category:Workshops]]

@@ Line 27: / Line 27: @@
 == Organizers ==
-* Amélie Anglade - Centre for Digital Music, Queen Mary University of London, UK (2010)
-* Claudio Baccigalupo - Ph.D. at the Artificial Intelligence Research Institute, IIIA-CSIC, Barcelona, Spain (2010)
+=== 2010 ===
-* Norman Casagrande - [[last.fm]], London, UK (2010)
-* Òscar Celma - BMAT, Barcelona, Spain (2010)
+* Amélie Anglade - Centre for Digital Music, Queen Mary University of London, UK
-* Paul Lamere - [[The Echo Nest]], Somerville, MA, US (2010)
+* Claudio Baccigalupo - Ph.D. at the Artificial Intelligence Research Institute, IIIA-CSIC, Barcelona, Spain
 == External links ==

@@ Line 1: / Line 1: @@
 == 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.
@@ Line 9: / Line 7: @@
 == 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
+* [[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
+* Novelty, familiarity and [[serendipity]] in music recommendation and discovery
-* Exploration and discovery in large music collections
+* [[Exploration]] and discovery in large music collections
-* Evaluation of music recommender systems
+* [[:Category: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
@@ Line 26: / Line 23: @@
 * Innovative music recommendation applications
 * Interfaces for music recommendation and discovery systems
-* Scalability issues and solutions
+* [[Scalability]] issues and solutions
 * Semantic Web, Linking Open Data and Open Web Services for music recommendation and discovery
 == Organizers ==
 * Amélie Anglade - Centre for Digital Music, Queen Mary University of London, UK (2010)
 * Claudio Baccigalupo - Ph.D. at the Artificial Intelligence Research Institute, IIIA-CSIC, Barcelona, Spain (2010)
-* Norman Casagrande - last.fm, London, UK (2010)
+* Norman Casagrande - [[last.fm]], London, UK (2010)
 * Òscar Celma - BMAT, Barcelona, Spain (2010)
-* Paul Lamere - The Echo Nest, Somerville, MA, US (2010)
+* Paul Lamere - [[The Echo Nest]], Somerville, MA, US (2010)
 [[Category:Workshops]]