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Web 3.0 in action: Vector Space Model for semantic (movie) Recommendations

Published: 26 March 2012 Publication History

Abstract

In this paper we present MORE (acronym of MORE than MOvie REcommendation), a Facebook application that semantically recommends movies to the user leveraging the knowledge within Linked Data and the information elicited from her profile. MORE exploits the power of social knowledge bases (e.g. DBpedia) to detect semantic similarities among movies. These similarities are computed by a Semantic version of the classical Vector Space Model (sVSM), applied to semantic datasets. MORE is freely available as a Facebook application.

References

[1]
R. A. Baeza-Yates and B. Ribeiro-Neto. Modern Information Retrieval: The Concepts and Technology behind Search. Addison-Wesley Professional, 2011.
[2]
C. Bizer, J. Lehmann, G. Kobilarov, S. Auer, C. Becker, R. Cyganiak, and S. Hellmann. Dbpedia - a crystallization point for the web of data. Web Semant., 7: 154--165, September 2009.
[3]
O. Hassanzadeh and M. P. Consens. Linked Movie Data Base. In Proceedings of the WWW2009 Workshop on Linked Data on the Web (LDOW2009), April 2009.
[4]
F. Ricci, L. Rokach, B. Shapira, and P. B. Kantor, editors. Recommender Systems Handbook. Springer, 2011.
[5]
G. Salton, A. Wong, and C. S. Yang. A vector space model for automatic indexing. Commun. ACM, 18: 613--620, November 1975.

Cited By

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  • (2018)A Linked Data Browser with Recommendations2018 IEEE 30th International Conference on Tools with Artificial Intelligence (ICTAI)10.1109/ICTAI.2018.00038(189-196)Online publication date: Nov-2018

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Published In

cover image ACM Conferences
SAC '12: Proceedings of the 27th Annual ACM Symposium on Applied Computing
March 2012
2179 pages
ISBN:9781450308571
DOI:10.1145/2245276
  • Conference Chairs:
  • Sascha Ossowski,
  • Paola Lecca

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 26 March 2012

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SAC 2012
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SAC 2012: ACM Symposium on Applied Computing
March 26 - 30, 2012
Trento, Italy

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SAC '12 Paper Acceptance Rate 270 of 1,056 submissions, 26%;
Overall Acceptance Rate 1,650 of 6,669 submissions, 25%

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  • (2018)A Linked Data Browser with Recommendations2018 IEEE 30th International Conference on Tools with Artificial Intelligence (ICTAI)10.1109/ICTAI.2018.00038(189-196)Online publication date: Nov-2018

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