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Folksonomies, the Semantic Web, and Movie Recommendation

by: Martin Szomszor, Ciro Cattuto, Harith Alani, Kieron O'Hara, Andrea Baldassarri, Vittorio Loreto, and Vito D.P. Servedio

In: 4th European Semantic Web Conference, Bridging the Gap between Semantic Web and Web 2.0 (2007) .

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URL:	http://eprints.ecs.soton.ac.uk/14007/

While the Semantic Web has evolved to support the meaningful exchange of heterogeneous data through shared and controlled conceptualisations, Web 2.0 has demonstrated that large-scale community tagging sites can enrich the semantic web with readily accessible and valuable knowledge. In this paper, we investigate the integration of a movies folksonomy with a semantic knowledge base about user-movie rentals. The folksonomy is used to enrich the knowledge base with descriptions and categorisations of movie titles, and user interests and opinions. Using tags harvested from the Internet Movie Database, and movie rating data gathered by Netﬂix, we perform experiments to investigate the question that folksonomy-generated movie tag-clouds can be used to construct better user proﬁles that reﬂect a user’s level of interest in different kinds of movies, and therefore, provide a basis for prediction of their rating for a previously unseen movie.

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Has an overview of the usage of tags.

BibTeX record

@inproceedings{ecs14007,
  abstract = {While the Semantic Web has evolved to support the meaningful exchange of heterogeneous data through shared and controlled conceptualisations, Web 2.0 has demonstrated that large-scale community tagging sites can enrich the semantic web with readily accessible and valuable knowledge. In this paper, we investigate the integration of a movies folksonomy with a semantic knowledge base about user-movie rentals. The folksonomy is used to enrich the knowledge base with descriptions and categorisations of movie titles, and user interests and opinions. Using tags harvested from the Internet Movie Database, and movie rating data gathered by Netﬂix, we perform experiments to investigate the question that folksonomy-generated movie tag-clouds can be used to construct better user proﬁles that reﬂect a user’s level of interest in different kinds of movies, and therefore, provide a basis for prediction of their rating for a previously unseen movie.},
  added-at = {2008-06-08T12:59:40.000+0200},
  author = {Szomszor, Martin and Cattuto, Ciro and Alani, Harith and O'Hara, Kieron and Baldassarri, Andrea and Loreto, Vittorio and Servedio, Vito D.P.},
  biburl = {http://www.bibsonomy.org/bibtex/2e4cc0d7b14b091f64d1377fb8625f92e/cschenk},
  booktitle = {4th European Semantic Web Conference, Bridging the Gap between Semantic Web and Web 2.0},
  description = {Has an overview of the usage of tags.},
  interhash = {1f9f83649c25b88e43bbb54494496be8},
  intrahash = {e4cc0d7b14b091f64d1377fb8625f92e},
  keywords = {collaborative filtering folksonomies imdb kind-of-tags movies netflix paper read:2008 recommendation semantic tagora tags web web2.0},
  timestamp = {2008-06-08T12:59:40.000+0200},
  title = {Folksonomies, the Semantic Web, and Movie Recommendation },
  url = {http://eprints.ecs.soton.ac.uk/14007/},
  year = 2007
}

Endnote record

%0 Conference Paper
%1 ecs14007
%A Szomszor, Martin
%A Cattuto, Ciro
%A Alani, Harith
%A O'Hara, Kieron
%A Baldassarri, Andrea
%A Loreto, Vittorio
%A Servedio, Vito D.P.
%B 4th European Semantic Web Conference, Bridging the Gap between Semantic Web and Web 2.0
%D 2007
%K 
%T Folksonomies, the Semantic Web, and Movie Recommendation 
%U http://eprints.ecs.soton.ac.uk/14007/
%X While the Semantic Web has evolved to support the meaningful exchange of heterogeneous data through shared and controlled conceptualisations, Web 2.0 has demonstrated that large-scale community tagging sites can enrich the semantic web with readily accessible and valuable knowledge. In this paper, we investigate the integration of a movies folksonomy with a semantic knowledge base about user-movie rentals. The folksonomy is used to enrich the knowledge base with descriptions and categorisations of movie titles, and user interests and opinions. Using tags harvested from the Internet Movie Database, and movie rating data gathered by Netﬂix, we perform experiments to investigate the question that folksonomy-generated movie tag-clouds can be used to construct better user proﬁles that reﬂect a user’s level of interest in different kinds of movies, and therefore, provide a basis for prediction of their rating for a previously unseen movie.