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Stefan cel Mare
University of Suceava
Faculty of Electrical Engineering and
Computer Science
13, Universitatii Street
Suceava - 720229

Print ISSN: 1582-7445
Online ISSN: 1844-7600
WorldCat: 643243560
doi: 10.4316/AECE


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  1/2014 - 18


TV Recommendation and Personalization Systems: Integrating Broadcast and Video On demand Services

SOARES, M. See more information about SOARES, M. on SCOPUS See more information about SOARES, M. on IEEExplore See more information about SOARES, M. on Web of Science, VIANA, P. See more information about VIANA, P. on SCOPUS See more information about VIANA, P. on SCOPUS See more information about VIANA, P. on Web of Science
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Download PDF pdficon (804 KB) | Citation | Downloads: 531 | Views: 3,092

Author keywords
collaborative filtering, content filtering, recommendation systems, TV-Anytime

References keywords
recommendation(6), user(5), systems(4)
Blue keywords are present in both the references section and the paper title.

About this article
Date of Publication: 2014-02-28
Volume 14, Issue 1, Year 2014, On page(s): 115 - 120
ISSN: 1582-7445, e-ISSN: 1844-7600
Digital Object Identifier: 10.4316/AECE.2014.01018
Web of Science Accession Number: 000332062300018
SCOPUS ID: 84894614863

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The expansion of Digital Television and the convergence between conventional broadcasting and television over IP contributed to the gradual increase of the number of available channels and on demand video content. Moreover, the dissemination of the use of mobile devices like laptops, smartphones and tablets on everyday activities resulted in a shift of the traditional television viewing paradigm from the couch to everywhere, anytime from any device. Although this new scenario enables a great improvement in viewing experiences, it also brings new challenges given the overload of information that the viewer faces. Recommendation systems stand out as a possible solution to help a watcher on the selection of the content that best fits his/her preferences. This paper describes a web based system that helps the user navigating on broadcasted and online television content by implementing recommendations based on collaborative and content based filtering. The algorithms developed estimate the similarity between items and users and predict the rating that a user would assign to a particular item (television program, movie, etc.). To enable interoperability between different systems, programs characteristics (title, genre, actors, etc.) are stored according to the TV-Anytime standard. The set of recommendations produced are presented through a Web Application that allows the user to interact with the system based on the obtained recommendations.

References | Cited By  «-- Click to see who has cited this paper

[1] G. Adomavicius, Y. Kwon, "New Recommendation Techniques for Multicriteria Rating Systems". IEEE Intelligent Systems, vol. 22, no. 3, pp. 48-55, 2007.
[CrossRef] [Web of Science Times Cited 208] [SCOPUS Times Cited 318]

[2] J. Bar-Ilan, K Keenoy, E. Yaari, M. Levene, "User rankings of search results". Journal of the American Society for Information Science and Technology, vol. 58, no. 9, pp. 1254-1266, May 2007.
[CrossRef] [Web of Science Times Cited 30] [SCOPUS Times Cited 42]

[3] J. Bar-Ilan, M. Mat-Hassan, M. Levene, "Methods for comparing rankings of search engine results". Computer Networks, vol. 50, no. 10, pp. 1448-1463, July 2006.
[CrossRef] [Web of Science Times Cited 68] [SCOPUS Times Cited 90]

[4] T. Burke, "Hybrid recommender systems: Survey and experiments". Modelling and User-Adapted Interaction, vol. 12 , no. 4, pp. 331-370, November 2002.
[CrossRef] [Web of Science Times Cited 1593] [SCOPUS Times Cited 2375]

[5] P. Cotter, B. Smith, Barry, "PTV: Intelligent Personalised TV Guides". In: Proceedings of the 12th Innovative Applications of Artificial Intelligence Conference, pp. 957-964, 2000.

[6] G. Holbling, M. Pleschgatternig, H. Kosch, "PersonalTV - A TV recommendation system using program metadata for content filtering". Multimedia Tools Application, vol. 46, no. 2, pp. 259-288, January 2010.
[CrossRef] [Web of Science Times Cited 3] [SCOPUS Times Cited 7]

[7] S. H. Hsu, M. H. Wen, H. C. Lin, C. C. Lee, C. H. Lee, "AIMED-A personalized TV Recommendation System". In Proceedings of the Interactive TV: A Shared Experience, 5th European Conference, vol. 4471, pp. 166-174, 2007. Springer Berlin/Heidelberg.

[8] J. B. Schafer, J. A. Konstan, J. Riedl, "E Recommendation Applications". GroupLens Research Project, Department of Computer Science and Engineering University of Minnesota, 2001.

[9] S. Velusamy, L. Gopal, S. Bhatnagar, S. Varadarajan, An efficient ad recommendation system for TV programs. Multimedia Systems, vol. 14 no. 2, pp. 73-87, 2008, Springer.
[CrossRef] [Web of Science Times Cited 16] [SCOPUS Times Cited 25]

[10] X. Su, T. M. Khoshgoftaar, "A Survey of Collaborative Filtering". Journal Advances in Artificial Intelligence archive, January 2009.

[11] Z. Yu, X. Zhou, Y. Hao, J. Gu, "TV program recommendation for multiple viewers based on user profile merging". User Modeling and User Adapted Interaction, vol. 16, no. 1, pp. 62-82, 2006.
[CrossRef] [Web of Science Times Cited 183] [SCOPUS Times Cited 261]

References Weight

Web of Science® Citations for all references: 2,101 TCR
SCOPUS® Citations for all references: 3,118 TCR

Web of Science® Average Citations per reference: 175 ACR
SCOPUS® Average Citations per reference: 260 ACR

TCR = Total Citations for References / ACR = Average Citations per Reference

We introduced in 2010 - for the first time in scientific publishing, the term "References Weight", as a quantitative indication of the quality ... Read more

Citations for references updated on 2021-02-25 00:17 in 48 seconds.

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