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

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


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  1/2014 - 18

 HIGH-IMPACT PAPER 

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: 970 | Views: 4,375

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

Abstract
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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

Cited-By Clarivate Web of Science

Web of Science® Times Cited: 8 [View]
View record in Web of Science® [View]
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Updated 2 days, 12 hours ago


Cited-By SCOPUS

SCOPUS® Times Cited: 14
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Cited-By CrossRef

[1] Data-driven personalisation of television content: a survey, Nixon, Lyndon, Foss, Jeremy, Apostolidis, Konstantinos, Mezaris, Vasileios, Multimedia Systems, ISSN 0942-4962, Issue 6, Volume 28, 2022.
Digital Object Identifier: 10.1007/s00530-022-00926-6
[CrossRef]

[2] Mass Media Deploying Digital Personalization: An Empirical Investigation, Loebbecke, Claudia, Oberschulte, Franziska, Boboschko, Irina, International Journal on Media Management, ISSN 1424-1277, Issue 3-4, Volume 23, 2021.
Digital Object Identifier: 10.1080/14241277.2022.2038605
[CrossRef]

[3] A Hybrid Approach for Personalized News Recommendation in a Mobility Scenario Using Long-Short User Interest, Viana, Paula, Soares, Márcio, International Journal on Artificial Intelligence Tools, ISSN 0218-2130, Issue 02, Volume 26, 2017.
Digital Object Identifier: 10.1142/S0218213017600120
[CrossRef]

[4] Tuning metadata for better movie content-based recommendation systems, Soares, Márcio, Viana, Paula, Multimedia Tools and Applications, ISSN 1380-7501, Issue 17, Volume 74, 2015.
Digital Object Identifier: 10.1007/s11042-014-1950-1
[CrossRef]

[5] Consumer Attitudes toward News Delivering: An Experimental Evaluation of the Use and Efficacy of Personalized Recommendations, Viana, Paula, Soares, Márcio, Gaio, Rita, Correia, Amilcar, Information, ISSN 2078-2489, Issue 7, Volume 11, 2020.
Digital Object Identifier: 10.3390/info11070350
[CrossRef]

[6] A literature review of recommender systems in the television domain, Véras, Douglas, Prota, Thiago, Bispo, Alysson, Prudêncio, Ricardo, Ferraz, Carlos, Expert Systems with Applications, ISSN 0957-4174, Issue 22, Volume 42, 2015.
Digital Object Identifier: 10.1016/j.eswa.2015.06.052
[CrossRef]

[7] Ontology Matched Cross Domain Personalized Recommendation of Tourist Attractions, Valliyammai, C., Ephina Thendral, S., Wireless Personal Communications, ISSN 0929-6212, Issue 1, Volume 107, 2019.
Digital Object Identifier: 10.1007/s11277-019-06290-5
[CrossRef]

[8] A hybrid recommendation system for news in a mobile environment, Viana, Paula, Soares, Márcio, Proceedings of the 6th International Conference on Web Intelligence, Mining and Semantics, ISBN 9781450340564, 2016.
Digital Object Identifier: 10.1145/2912845.2912852
[CrossRef]

[9] The SOM Based Improved K-Means Clustering Collaborative Filtering Algorithm in TV Recommendation System, Ma, Zhaocai, Yang, Yi, Wang, Fei, Li, Caihong, Li, Lian, 2014 Second International Conference on Advanced Cloud and Big Data, ISBN 978-1-4799-8085-7, 2014.
Digital Object Identifier: 10.1109/CBD.2014.45
[CrossRef]

Updated 2 days, 12 hours ago

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