Cited By
View all- Schramm EYang CChang CMulhorn KYoshinaga SHuh-Yoo J(2023)Examining Public Awareness of Ageist Terms on Twitter: Content AnalysisJMIR Aging10.2196/414486(e41448-e41448)Online publication date: 11-Sep-2023
Modeling, characterizing and recommendation in multimedia web content services
Pages 265 - 268
Abstract
Web content has gained much importance lately. One of the most important content types is online video, as demonstrated by the success of platforms such as YouTube. The growth in the volume of available online video is also observed in corporate scenarios, such as TV networks. This paper evaluates a set of corporate online videos hosted by Sambatech, a company that holds the largest platform for online multimedia content distribution in Latin America. We propose a novel analytical approach for video recommendation, focusing on video objects being consumed, and not on consumer profile data. After modeling this service, we characterize the contents from multiple sources, and propose techniques for video recommendation. Experimental results indicate that the proposed method obtains a gain of about 42% in precision for a set of five recommendations, as compared to a baseline that is based only on video metadata.
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Index Terms
- Modeling, characterizing and recommendation in multimedia web content services
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Modeling, Characterization and Recommendation of Multimedia Web Content Services
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Information & Contributors
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Published In
November 2013
360 pages
ISBN:9781450325592
DOI:10.1145/2526188
- General Chairs:
- Cássio V.S. Prazeres,
- Paulo N.M. Sampaio,
- Program Chairs:
- André Santanchè,
- Celso A.S. Santos,
- Rudinei Goularte
Copyright © 2013 ACM.
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- SBC: Brazilian Computer Society
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Association for Computing Machinery
New York, NY, United States
Publication History
Published: 05 November 2013
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- Research-article
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WebMedia '13
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- SBC
WebMedia '13: 19th Brazilian Symposium on Multimedia and the Web
November 5 - 8, 2013
Salvador, Brazil
Acceptance Rates
WebMedia '13 Paper Acceptance Rate 29 of 87 submissions, 33%;
Overall Acceptance Rate 270 of 873 submissions, 31%
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View all- Schramm EYang CChang CMulhorn KYoshinaga SHuh-Yoo J(2023)Examining Public Awareness of Ageist Terms on Twitter: Content AnalysisJMIR Aging10.2196/414486(e41448-e41448)Online publication date: 11-Sep-2023
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