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Topic-STG: extending the session-based temporal graph approach for personalized tweet recommendation

Authors:

Jianjun Yu,

Yi Shen,

Zhenglu YangAuthors Info & Claims

WWW '14 Companion: Proceedings of the 23rd International Conference on World Wide Web

Pages 413 - 414

https://doi.org/10.1145/2567948.2577328

Published: 07 April 2014 Publication History

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Abstract

Micro-blogging is experiencing fantastic success in the worldwide. However, during its rapid development, it has encountered the problem of information overload, which has troubled many users. In this paper, we mainly focus on the task of tweet recommendation to address this problem. We extend the session-based temporal graph (STG) approach as Topic-STG for tweet recommendation which comprehensively considers three types of features in Twitter: the textual information, the time factor, and the users' behavior. The experimental results conducted on a real dataset demonstrate the effectiveness of our approach.

References

[1]

K. Chen, T. Chen, and et al. Collaborative personalized tweet recommendation. In SIGIR'12 Conference Proceedings, pages 661--670, 2012.

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[2]

X. Liang, Y. Quan, and et al. Temporal recommendation on graphs via long-and short-term preference fusion. In KDD'10 Conference Proceedings, pages 723--732, 2010.

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[3]

P. Marco, S. Fabrizio, and et al. Making your interests follow you on twitter. In CIKM'12 Conference Proceedings, pages 165--174, 20

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Cited By

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Bin Suhaim ABerri J(2022)Directional user similarity model for personalized recommendation in online social networksJournal of King Saud University - Computer and Information Sciences10.1016/j.jksuci.2022.10.01734:10(10205-10216)Online publication date: Nov-2022
https://doi.org/10.1016/j.jksuci.2022.10.017
Taheri MFarnaghi MAlimohammadi AMoradi PKhoshahval S(2021)Point-of-interest recommendation using extended random walk with restart on geographical-temporal hybrid tripartite graphJournal of Spatial Science10.1080/14498596.2021.189639268:1(71-89)Online publication date: 22-Mar-2021
https://doi.org/10.1080/14498596.2021.1896392
Sun XTan YWu QWang JShen C(2019)New Algorithms for Counting Temporal Graph PatternSymmetry10.3390/sym1110118811:10(1188)Online publication date: 20-Sep-2019
https://doi.org/10.3390/sym11101188
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Index Terms

Topic-STG: extending the session-based temporal graph approach for personalized tweet recommendation
1. Information systems
  1. Information retrieval
    1. Retrieval tasks and goals
      1. Document filtering
      2. Information extraction
  2. Information systems applications
    1. Data mining

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Information & Contributors

Information

Published In

WWW '14 Companion: Proceedings of the 23rd International Conference on World Wide Web

April 2014

1396 pages

ISBN:9781450327459

DOI:10.1145/2567948

General Chair:
Chin-Wan Chung
Korea Advanced Institute of Science and Technology, Korea
,
Program Chairs:
Andrei Broder
Google Inc., USA
,
Kyuseok Shim
Seoul National University, Korea
,
Torsten Suel
New York University, USA

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.

In-Cooperation

SIGWEB: ACM Special Interest Group on Hypertext, Hypermedia, and Web

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 07 April 2014

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WWW '14

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IW3C2

WWW '14: 23rd International World Wide Web Conference

April 7 - 11, 2014

Seoul, Korea

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Overall Acceptance Rate 1,899 of 8,196 submissions, 23%

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Bin Suhaim ABerri J(2022)Directional user similarity model for personalized recommendation in online social networksJournal of King Saud University - Computer and Information Sciences10.1016/j.jksuci.2022.10.01734:10(10205-10216)Online publication date: Nov-2022
https://doi.org/10.1016/j.jksuci.2022.10.017
Taheri MFarnaghi MAlimohammadi AMoradi PKhoshahval S(2021)Point-of-interest recommendation using extended random walk with restart on geographical-temporal hybrid tripartite graphJournal of Spatial Science10.1080/14498596.2021.189639268:1(71-89)Online publication date: 22-Mar-2021
https://doi.org/10.1080/14498596.2021.1896392
Sun XTan YWu QWang JShen C(2019)New Algorithms for Counting Temporal Graph PatternSymmetry10.3390/sym1110118811:10(1188)Online publication date: 20-Sep-2019
https://doi.org/10.3390/sym11101188
Sun XTan YWu QChen BShen C(2019)TM-Miner: TFS-Based Algorithm for Mining Temporal Motifs in Large Temporal NetworkIEEE Access10.1109/ACCESS.2019.29111817(49778-49789)Online publication date: 2019
https://doi.org/10.1109/ACCESS.2019.2911181
Xu YYu QLam WLin T(2017)Exploiting interactions of review text, hidden user communities and item groups, and time for collaborative filteringKnowledge and Information Systems10.1007/s10115-016-1005-152:1(221-254)Online publication date: 1-Jul-2017
https://dl.acm.org/doi/10.1007/s10115-016-1005-1
Yin HCui BChen LHu ZZhou X(2015)Dynamic User Modeling in Social Media SystemsACM Transactions on Information Systems10.1145/269967033:3(1-44)Online publication date: 9-Mar-2015
https://dl.acm.org/doi/10.1145/2699670

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