RecSysWiki - User contributions [en]

Python-recsys

2012-01-20T15:11:50Z

Ocelma: /* External links */ misspelled Divisi2

'''python-recsys''' is a fast recommender engine for [[Python]].
It uses [[matrix factorization]] to provide recommendations and [[similiarities]] among items or users.
The library is built on top of divisi2, which already implements [[SVD]]-like matrix factorization using numpy.

== Quick Start ==

See some examples to see how simple is to build a recommender system in Python!

* http://ocelma.net/software/python-recsys/build/html/quickstart.html

== Documentation ==

* http://ocelma.net/software/python-recsys/build/html/

== External links ==

* https://github.com/ocelma/python-recsys official website
* http://csc.media.mit.edu/docs/divisi2/ Divisi2 Package from MIT

[[Category: Software]]

Python-recsys

2012-01-19T19:41:56Z

Ocelma:

[https://github.com/ocelma/python-recsys '''python-recsys'''] is a fast recommender engine for Python.
It uses Matrix Factorization to provide recommendations and similiarities among items or users.
The library is built on top of divisi2, which already implements SVD-like matrix factorization using numpy.

== Quick Start ==

See some examples to see how simple is to build a recommender system in Python!

* http://ocelma.net/software/python-recsys/build/html/quickstart.html

== Documentation ==

* http://ocelma.net/software/python-recsys/build/html/

== External links ==

* https://github.com/ocelma/python-recsys official website
* http://csc.media.mit.edu/docs/divisi2/ Divis2 Package from MIT

[[Category:Software]]

Python-recsys

2012-01-19T19:37:17Z

Ocelma:

'''python-recsys''' is a fast recommender engine for Python.
It uses Matrix Factorization to provide recommendations and similiarities among items or users.
The library is built on top of divisi2, which already implements SVD-like matrix factorization using numpy.

== Quick Start ==

See some examples to see how simple is to build a recommender system in Python!

* http://ocelma.net/software/python-recsys/build/html/quickstart.html

== Documentation ==

* http://ocelma.net/software/python-recsys/build/html/

== External links ==

* https://github.com/ocelma/python-recsys official website
* http://csc.media.mit.edu/docs/divisi2/ Divis2 Package from MIT

[[Category:Software]]

Python-recsys

2012-01-19T19:33:57Z

Ocelma: added python-recsys page

'''python-recsys''' is a fast recommender engine for Python.
It uses Matrix Factorization to provide recommendations and similiarities among items or users.
The library is built on top of divisi2, which already implements SVD-like matrix factorization using numpy.

== Quick Start ==

See some examples to see how simple is to start building a recommender systm in Python!

* http://ocelma.net/software/python-recsys/build/html/quickstart.html

== Documentation ==

* http://ocelma.net/software/python-recsys/build/html/

== External links ==

* https://github.com/ocelma/python-recsys official website
* http://csc.media.mit.edu/docs/divisi2/ Divis2 Package from MIT

[[Category:Software]]

User talk:Ocelma

2011-09-23T22:29:21Z

Ocelma:

Oscar Celma from Gracenote, San Francisco, US

* [http://ocelma.net homepage]
* [http://twitter.com/ocelma Twitter: @ocelma]
* [https://github.com/ocelma github: ocelma]

User talk:Ocelma

2011-09-23T22:28:13Z

Ocelma:

Oscar Celma from Gracenote, San Francisco, US

* [http://ocelma.net]
* [http://twitter.com/ocelma Twitter: @ocelma]
* [https://github.com/ocelma github: ocelma]

Womrad

2011-06-10T03:39:04Z

Ocelma: /* 2010 */

== 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
* [[: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
* 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

== External links ==
* http://womrad.org

[[Category: Workshop]]
[[Category: Music recommendation]]

Womrad

2011-06-10T03:38:49Z

Ocelma: /* 2011 */

== 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
* [[: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
* 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

== External links ==
* http://womrad.org

[[Category: Workshop]]
[[Category: Music recommendation]]

Womrad

2011-06-10T03:38:25Z

Ocelma: /* Organizers */ added 2011

== 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
* [[: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
* 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

== External links ==
* http://womrad.org

[[Category: Workshop]]
[[Category: Music recommendation]]

Womrad

2011-03-23T22:35:32Z

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..."

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)