In this paper there is an overview of the Foafing the Music system. The system uses the Friend of a Friend (FOAF) and RDF Site Summary (RSS) vocabularies for recommending music to a user, depending on the user's musical tastes and listening habits. Music information(new album releases, podcast sessions, audio from MP3 blogs, related artists' news and upcoming gigs) is gathered from thousands ofRSS feeds.
The presented system provides music discovery by means of: user profiling (defined in the user's FOAF description), context based information (extracted from music related RSS feeds) and content based descriptions(extracted from the audio itself), based on a common ontology (OWLDL) that describes the music domain.The system is available at: http://foafing-the-music.iua.upf.edu
The link to the paper :http://iswc2006.semanticweb.org/items/swchallenge_celma.pdf
Thursday, February 21, 2008
Thursday, February 14, 2008
Application of Dimensionality Reduction in Recommender System
This paper presents two different experiments where
one technology called Singular Value Decomposition (SVD)
is explored to reduce the
dimensionality of recommender system databases.
Each experiment compares the quality of a
recommender system using SVD with the quality of a
recommender system using collaborative filtering.
The first experiment compares the effectiveness of
the two recommender systems at predicting consumer
preferences based on a database of explicit ratings of
products. The second experiment compares the
effectiveness of the two recommender systems at
producing Top-N lists based on a real-life customer
purchase database from an E-Commerce site.
Please see the link:
http://www.grouplens.org/papers/pdf/webKDD00.pdf
one technology called Singular Value Decomposition (SVD)
is explored to reduce the
dimensionality of recommender system databases.
Each experiment compares the quality of a
recommender system using SVD with the quality of a
recommender system using collaborative filtering.
The first experiment compares the effectiveness of
the two recommender systems at predicting consumer
preferences based on a database of explicit ratings of
products. The second experiment compares the
effectiveness of the two recommender systems at
producing Top-N lists based on a real-life customer
purchase database from an E-Commerce site.
Please see the link:
http://www.grouplens.org/papers/pdf/webKDD00.pdf
Thursday, February 7, 2008
Hybrid Recommender Systems
Recommender systems represent user preferences for the purpose of suggesting
items to purchase or examine. They have become fundamental applications in
electronic commerce and information access, providing suggestions that
effectively prune large information spaces so that users are directed toward those
items that best meet their needs and preferences. A variety of techniques have
been proposed for performing recommendation, including content-based,
collaborative, knowledge-based and other techniques. To improve performance,
these methods have sometimes been combined in hybrid recommenders. This
paper surveys the landscape of actual and possible hybrid recommenders, and
introduces a novel hybrid, EntreeC, a system that combines knowledge-based
recommendation and collaborative filtering to recommend restaurants. Further,
we show that semantic ratings obtained from the knowledge-based part of the
system enhance the effectiveness of collaborative filtering.
All of the known recommendation techniques have strengths and weaknesses, and manyresearchers have chosen to combine techniques in different ways. This article surveys the different recommendationtechniques being researched — analyzing them in terms of the data that supports the recommendations and thealgorithms that operate on that data — and examines the range of hybridization techniques that have been proposed.This analysis points to a number of possible hybrids that have yet to be explored. Finally, how adding ahybrid with collaborative filtering improved the performance of our knowledge-based recommender system Entree is discussed.
This links to the paper:
http://www-mmt.inf.tu-dresden.de/Lehre/Archiv/Sommersemester_04/Hauptseminar/papers/burke2002.pdf
items to purchase or examine. They have become fundamental applications in
electronic commerce and information access, providing suggestions that
effectively prune large information spaces so that users are directed toward those
items that best meet their needs and preferences. A variety of techniques have
been proposed for performing recommendation, including content-based,
collaborative, knowledge-based and other techniques. To improve performance,
these methods have sometimes been combined in hybrid recommenders. This
paper surveys the landscape of actual and possible hybrid recommenders, and
introduces a novel hybrid, EntreeC, a system that combines knowledge-based
recommendation and collaborative filtering to recommend restaurants. Further,
we show that semantic ratings obtained from the knowledge-based part of the
system enhance the effectiveness of collaborative filtering.
All of the known recommendation techniques have strengths and weaknesses, and manyresearchers have chosen to combine techniques in different ways. This article surveys the different recommendationtechniques being researched — analyzing them in terms of the data that supports the recommendations and thealgorithms that operate on that data — and examines the range of hybridization techniques that have been proposed.This analysis points to a number of possible hybrids that have yet to be explored. Finally, how adding ahybrid with collaborative filtering improved the performance of our knowledge-based recommender system Entree is discussed.
This links to the paper:
http://www-mmt.inf.tu-dresden.de/Lehre/Archiv/Sommersemester_04/Hauptseminar/papers/burke2002.pdf
Subscribe to:
Posts (Atom)
