research-article

SocialTransfer: cross-domain transfer learning from social streams for media applications

Authors:

Shipeng LiAuthors Info & Claims

MM '12: Proceedings of the 20th ACM international conference on Multimedia

Pages 649 - 658

https://doi.org/10.1145/2393347.2393437

Published: 29 October 2012 Publication History

Abstract

The usage and applications of social media have become pervasive. This has enabled an innovative paradigm to solve multimedia problems (e.g., recommendation and popularity prediction), which are otherwise hard to address purely by traditional approaches. In this paper, we investigate how to build a mutual connection among the disparate social media on the Internet, using which cross-domain media recommendation can be realized. We accomplish this goal through SocialTransfer---a novel cross-domain real-time transfer learning framework. While existing transfer learning methods do not address how to utilize the real time social streams, our proposed SocialTransfer is able to effectively learn from social streams to help multimedia applications, assuming an intermediate topic space can be built across domains. It is characterized by two key components: 1) a topic space learned in real time from social streams via Online Streaming Latent Dirichlet Allocation (OSLDA), and 2) a real-time cross-domain graph spectra analysis based transfer learning method that seamlessly incorporates learned topic models from social streams into the transfer learning framework. We present as use cases of \emph{SocialTransfer} two video recommendation applications that otherwise can hardly be achieved by conventional media analysis techniques: 1) socialized query suggestion for video search, and 2) socialized video recommendation that features socially trending topical videos. We conduct experiments on a real-world large-scale dataset, including 10.2 million tweets and 5.7 million YouTube videos and show that \emph{SocialTransfer} outperforms traditional learners significantly, and plays a natural and interoperable connection across video and social domains, leading to a wide variety of cross-domain applications.

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https://dl.acm.org/doi/10.1016/j.knosys.2023.110303
Khan AHasana MGhazal TIslam SAlzoubi HMokhtar UAlam RAhmad M(2022)Collaborative Learning Assessment via Information and Communication Technology2022 RIVF International Conference on Computing and Communication Technologies (RIVF)10.1109/RIVF55975.2022.10013841(311-316)Online publication date: 20-Dec-2022
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Index Terms

SocialTransfer: cross-domain transfer learning from social streams for media applications
1. Information systems
  1. World Wide Web
    1. Web applications
    2. Web services

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Published In

cover image ACM Conferences

MM '12: Proceedings of the 20th ACM international conference on Multimedia

October 2012

1584 pages

ISBN:9781450310895

DOI:10.1145/2393347

General Chairs:
Noboru Babaguchi
Osaka University, Japan
,
Kiyoharu Aizawa
The University of Tokyo, Japan
,
John Smith
IBM, USA
,
Program Chairs:
Shin'ichi Satoh
National Institute of Informatics, Japan
,
Thomas Plagemann
University of Oslo, Norway
,
Xian-Sheng Hua
Microsoft, USA
,
Rong Yan
Facebook, USA

Copyright © 2012 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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SIGMM: ACM Special Interest Group on Multimedia

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 29 October 2012

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MM '12

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SIGMM

MM '12: ACM Multimedia Conference

October 29 - November 2, 2012

Nara, Japan

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

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Li HHe FPan Y(2023)Multi-objective dynamic distribution adaptation with instance reweighting for transfer feature learningKnowledge-Based Systems10.1016/j.knosys.2023.110303263:COnline publication date: 5-Mar-2023
https://dl.acm.org/doi/10.1016/j.knosys.2023.110303
Khan AHasana MGhazal TIslam SAlzoubi HMokhtar UAlam RAhmad M(2022)Collaborative Learning Assessment via Information and Communication Technology2022 RIVF International Conference on Computing and Communication Technologies (RIVF)10.1109/RIVF55975.2022.10013841(311-316)Online publication date: 20-Dec-2022
https://doi.org/10.1109/RIVF55975.2022.10013841
Dai SMeng F(2022)Addressing modern and practical challenges in machine learning: a survey of online federated and transfer learningApplied Intelligence10.1007/s10489-022-04065-353:9(11045-11072)Online publication date: 30-Aug-2022
https://doi.org/10.1007/s10489-022-04065-3
Wang YChu YThaljaoui AKhan YChammam WAbbas S(2021)A multi-feature hybrid classification data mining technique for human-emotionBioData Mining10.1186/s13040-021-00254-x14:1Online publication date: 29-Mar-2021
https://doi.org/10.1186/s13040-021-00254-x
Kolluri ABaluta TSaxena PKim YKim JVigna GShi E(2021)Private Hierarchical Clustering in Federated NetworksProceedings of the 2021 ACM SIGSAC Conference on Computer and Communications Security10.1145/3460120.3484822(2342-2360)Online publication date: 12-Nov-2021
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Gao YSang JFu CWang ZRen TXu C(2021)Metadata Connector: Exploiting Hashtag and Tag for Cross-OSN Event SearchIEEE Transactions on Multimedia10.1109/TMM.2020.298204723(510-523)Online publication date: 2021
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Wen YCao XWang XLiu F(2020)Biased transfer matching for less overlapping degree for unsupervised domain adaptationIntelligent Data Analysis10.3233/IDA-19451624:2(409-425)Online publication date: 27-Mar-2020
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Zhu WWang XLi H(2020)Multi-Modal Deep Analysis for MultimediaIEEE Transactions on Circuits and Systems for Video Technology10.1109/TCSVT.2019.294064730:10(3740-3764)Online publication date: Oct-2020
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Bai WZhang YHuang WZhou YWu DLiu GXiao L(2020)DeepFusion: predicting movie popularity via cross-platform feature fusionMultimedia Tools and Applications10.1007/s11042-020-08730-yOnline publication date: 19-Mar-2020
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