research-article

An online video recommendation framework using rich information

Authors:

Zhoujun LiAuthors Info & Claims

ICIMCS '11: Proceedings of the Third International Conference on Internet Multimedia Computing and Service

Pages 46 - 50

https://doi.org/10.1145/2043674.2043688

Published: 05 August 2011 Publication History

Abstract

Automatic video recommendation is involved in an attempt to tackle the information-overload problem, aiming to present the personalized video list to the user. This paper presents a novel approach to improve the accuracy of the video recommendation by combining the content-based filtering (CBF) method and the collaborative filtering (CF) method. Multimodal information is utilized to calculate the similarity among different videos to overcome the sparseness problem by CF method. We conduct experiments on a dataset of more than 11,000 videos and the results demonstrate the feasibility and effectiveness of our approach.

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Cited By

Yoshida TIrie GArai HTaniguchi Y(2013)Towards semantic and affective content-based video recommendation2013 IEEE International Conference on Multimedia and Expo Workshops (ICMEW)10.1109/ICMEW.2013.6618331(1-6)Online publication date: Jul-2013
https://doi.org/10.1109/ICMEW.2013.6618331
Zhao XYuan JWang MLi GHong RLi ZChua T(2013)Video recommendation over multiple information sourcesMultimedia Systems10.1007/s00530-012-0267-z19:1(3-15)Online publication date: 1-Feb-2013
https://dl.acm.org/doi/10.1007/s00530-012-0267-z

Index Terms

An online video recommendation framework using rich information
1. Information systems
  1. World Wide Web
    1. Web applications
    2. Web services

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Information & Contributors

Information

Published In

cover image ACM Other conferences

ICIMCS '11: Proceedings of the Third International Conference on Internet Multimedia Computing and Service

August 2011

208 pages

ISBN:9781450309189

DOI:10.1145/2043674

General Chairs:
Changsheng Xu
Institute of Automation, CAS, China
,
Ralf Steinmetz
Darmstadt Technical University, Germany
,
Program Chairs:
Abdulmotaleb El Saddik
University of Ottawa, Canada
,
Chong-Wah Ngo
City University of Hong Kong, Hong Kong
,
Xiao Wu
Southwest Jiaotong University, China

Copyright © 2011 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Sichuan University
Chinese Academy of Sciences
SCF: Sichuan Computer Federation
Southwest Jiaotong University
Beijing ACM SIGMM Chapter

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 05 August 2011

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Beihang University

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ICIMCS '11

Sponsor:

SCF

ICIMCS '11: The Third International Conference on Internet Multimedia Computing and Service

August 5 - 7, 2011

Chengdu, China

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Overall Acceptance Rate 163 of 456 submissions, 36%

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Cited By

Yoshida TIrie GArai HTaniguchi Y(2013)Towards semantic and affective content-based video recommendation2013 IEEE International Conference on Multimedia and Expo Workshops (ICMEW)10.1109/ICMEW.2013.6618331(1-6)Online publication date: Jul-2013
https://doi.org/10.1109/ICMEW.2013.6618331
Zhao XYuan JWang MLi GHong RLi ZChua T(2013)Video recommendation over multiple information sourcesMultimedia Systems10.1007/s00530-012-0267-z19:1(3-15)Online publication date: 1-Feb-2013
https://dl.acm.org/doi/10.1007/s00530-012-0267-z

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