demonstration

A novel framework for collaborative video recommendation, interest discovery and friendship suggestion based on semantic profiling

Authors:

Daniele PezzatiniAuthors Info & Claims

MM '13: Proceedings of the 21st ACM international conference on Multimedia

Pages 451 - 452

https://doi.org/10.1145/2502081.2502264

Published: 21 October 2013 Publication History

Get Access

Abstract

Two important challenges for social networks are the creation of targeted and personalized content for their users, selecting the most interesting material from the huge amount of user-generated content, and keeping user engagement, e.g. through creation and curation of users' profiles. In this demo we show a system for video commenting, sharing and interest discovery that combines recommendation algorithms, clustering techniques, tools for video tagging and evaluation of semantic resources relatedness. Combining these tools and techniques it becomes possible to provide personalized multimedia services and to improve and propagate interests and inter-personal connections through the network.

Supplementary Material

suppl.mov (mm182de.mp4)

Supplemental video

Download
8.41 MB

References

[1]

Renjie Zhou, Samamon Khemmarat, and Lixin Gao. The impact of YouTube recommendation system on video views. In Proc. of ACM SIGCOMM IMC, 2010.

Digital Library

Google Scholar

[2]

James Davidson, Benjamin Liebald, Junning Liu, Palash Nandy, Taylor Van Vleet, Ullas Gargi, Sujoy Gupta, Yu He, Mike Lambert, Blake Livingston, and Dasarathi Sampath. The YouTube video recommendation system. In Proc. of ACM RecSys, 2010.

Digital Library

Google Scholar

[3]

S.J. Davis, I.S. Burnett, and C.H. Ritz. Using social networking and collections to enable video semantics acquisition. IEEE MultiMedia, 16(4):52--61, 2009.

Digital Library

Google Scholar

[4]

Shuang-Hong Yang, Bo Long, Alex Smola, Narayanan Sadagopan, Zhaohui Zheng, and Hongyuan Zha. Like like alike: joint friendship and interest propagation in social networks. In Proc. of WWW, 2011.

Digital Library

Google Scholar

[5]

David Milne and Ian H. Witten. Learning to link with Wikipedia. In Proc. of ACM CIKM, 2008.

Digital Library

Google Scholar

[6]

David Milne and I. H. Witten. An effective, low-cost measure of semantic relatedness obtained from Wikipedia links. In Proc. of AAAI WIKIAI, 2008.

Google Scholar

Cited By

View all

Kristoffersen MShepstone STan Z(2020)The Importance of Context When Recommending TV Content: Dataset and AlgorithmsIEEE Transactions on Multimedia10.1109/TMM.2019.294421422:6(1531-1541)Online publication date: Jun-2020
https://doi.org/10.1109/TMM.2019.2944214
Estrada LHildebrand Mde Boer Vvan Ossenbruggen J(2017)Time-based tags for fiction moviesJournal of the Association for Information Science and Technology10.1002/asi.2365668:2(348-364)Online publication date: 1-Feb-2017
https://dl.acm.org/doi/10.1002/asi.23656
Cui PWang ZSu ZHua KRui YSteinmetz RHanjalic ANatsev AZhu W(2014)What Videos Are Similar with You?Proceedings of the 22nd ACM international conference on Multimedia10.1145/2647868.2654946(597-606)Online publication date: 3-Nov-2014
https://dl.acm.org/doi/10.1145/2647868.2654946

Index Terms

A novel framework for collaborative video recommendation, interest discovery and friendship suggestion based on semantic profiling
1. Information systems
  1. World Wide Web
    1. Web applications
    2. Web services

Recommendations

Socially-aware video recommendation using users' profiles and crowdsourced annotations
SAM '13: Proceedings of the 2nd international workshop on Socially-aware multimedia

The recent explosive growth in the use of social networks has raised the question of how to meet the emerging demand for services that address the interests of the users. In this paper we show how considering homophily in social networks can improve ...
A social network for video annotation and discovery based on semantic profiling
WWW '12 Companion: Proceedings of the 21st International Conference on World Wide Web

This paper presents a system for the social annotation and discovery of videos based on social networks and social knowledge. The system, developed as a web application, allows users to comment and annotate, manually and automatically, video frames and ...
A framework for diversifying recommendation lists by user interest expansion

Recommender systems have been widely used to discover users' preferences and recommend interesting items to users during this age of information overload. Researchers in the field of recommender systems have realized that the quality of a top-N ...

Comments

Please enable JavaScript to view thecomments powered by Disqus.

Information & Contributors

Information

Published In

MM '13: Proceedings of the 21st ACM international conference on Multimedia

October 2013

1166 pages

ISBN:9781450324045

DOI:10.1145/2502081

General Chairs:
Alejandro (Alex) Jaimes
Yahoo!, Spain
,
Nicu Sebe
University of Trento, Italy
,
Nozha Boujemaa
INRIA, France
,
Program Chairs:
Daniel Gatica-Perez
IDIAP & EPFL, Switzerland
,
David A. Shamma
Yahoo!, USA
,
Marcel Worring
University of Amsterdam, The Netherlands
,
Roger Zimmermann
National University of Singapore, Singapore

Permission to make digital or hard copies of part or all 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 third-party components of this work must be honored. For all other uses, contact the Owner/Author.

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 21 October 2013

Check for updates

Author Tags

Qualifiers

Demonstration

Conference

MM '13

Sponsor:

SIGMM

MM '13: ACM Multimedia Conference

October 21 - 25, 2013

Barcelona, Spain

Acceptance Rates

MM '13 Paper Acceptance Rate 47 of 235 submissions, 20%;

Overall Acceptance Rate 2,145 of 8,556 submissions, 25%

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

3
Total Citations
View Citations
244
Total Downloads

Downloads (Last 12 months)1
Downloads (Last 6 weeks)0

Reflects downloads up to 13 Dec 2024

Other Metrics

View Author Metrics

Citations

Cited By

View all

Kristoffersen MShepstone STan Z(2020)The Importance of Context When Recommending TV Content: Dataset and AlgorithmsIEEE Transactions on Multimedia10.1109/TMM.2019.294421422:6(1531-1541)Online publication date: Jun-2020
https://doi.org/10.1109/TMM.2019.2944214
Estrada LHildebrand Mde Boer Vvan Ossenbruggen J(2017)Time-based tags for fiction moviesJournal of the Association for Information Science and Technology10.1002/asi.2365668:2(348-364)Online publication date: 1-Feb-2017
https://dl.acm.org/doi/10.1002/asi.23656
Cui PWang ZSu ZHua KRui YSteinmetz RHanjalic ANatsev AZhu W(2014)What Videos Are Similar with You?Proceedings of the 22nd ACM international conference on Multimedia10.1145/2647868.2654946(597-606)Online publication date: 3-Nov-2014
https://dl.acm.org/doi/10.1145/2647868.2654946

View Options

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Cited By

Index Terms

Recommendations

Socially-aware video recommendation using users' profiles and crowdsourced annotations

A social network for video annotation and discovery based on semantic profiling

A framework for diversifying recommendation lists by user interest expansion