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TV Recommendation and Personalization Systems: Integrating Broadcast and Video On demand ServicesSOARES, M.   , VIANA, P. |
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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
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. |
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[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, 11 hours ago
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Faculty of Electrical Engineering and Computer Science
Stefan cel Mare University of Suceava, Romania
All rights reserved: Advances in Electrical and Computer Engineering is a registered trademark of the Stefan cel Mare University of Suceava. No part of this publication may be reproduced, stored in a retrieval system, photocopied, recorded or archived, without the written permission from the Editor. When authors submit their papers for publication, they agree that the copyright for their article be transferred to the Faculty of Electrical Engineering and Computer Science, Stefan cel Mare University of Suceava, Romania, if and only if the articles are accepted for publication. The copyright covers the exclusive rights to reproduce and distribute the article, including reprints and translations.
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Disclaimer: Whilst every effort is made by the publishers and editorial board to see that no inaccurate or misleading data, opinions or statements appear in this journal, they wish to make it clear that all information and opinions formulated in the articles, as well as linguistic accuracy, are the sole responsibility of the author.
