research-article

Ranking and combining social network data for web personalization

Authors:

Yi Zeng,

Hongwei Hao,

Ning Zhong,

Xu Ren,

Yan WangAuthors Info & Claims

DUBMMSM '12: Proceedings of the 2012 workshop on Data-driven user behavioral modelling and mining from social media

Pages 15 - 18

https://doi.org/10.1145/2390131.2390139

Published: 29 October 2012 Publication History

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Abstract

Various Web-based social network data reflect user interests from multiple perspectives in a distributed environment. They need to be integrated for better user modelling and personalized services. We argue that in different scenarios, different social networks play different roles and their degrees of importance are not equivalent. Hence, ranking strategies among different social network data sources are needed. In addition, combining different social network data can produce interesting subsets of these data with different levels of importance. In this paper, we propose social network data ranking and composition strategies, we validate the proposed methods by collaboration network data (Semantic Web Dog Food) and micro-blogging data (from Twitter), then we use the ranked and composed results for developing a Web-based personalized academic visit recommendation system to show their potential effectiveness.

References

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D. Carmel, N. Zwerdling, I. Guy, S. Ofek-Koifman, N. Har'el, I. Ronen, E. Uziel, S. Yogev, and S. Chernov. Personalized social search based on the user's social network. In Proceedings of the 18th ACM conference on Information and knowledge management (CIKM), pages 1227--1236, 2009.

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[2]

Y. Zeng, Z. Huang, F. Liu, X. Ren, and N. Zhong. Interest logic and its application on the web. In Proceedings of the 5th International Conference on Knowledge Science, Engineering, and Management (KSEM 2011), pages 12--23, 2011.

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[3]

Y. Zeng, N. Zhong, X. Ren, and Y. Wang. User interests driven web personalization based on multiple social networks. In Proceedings of the 4th International Workshop on Web Intelligence & Communities, 2012.

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Y. Zeng, E. Zhou, Y. Wang, X. Ren, Y. Qin, Z. Huang, and N. Zhong. Research interests : Their dynamics, structures and applications in unifying search and reasoning. Journal of Intelligent Information Systems, 37(1):65--88, 2011.

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Cited By

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Vitkova LKotenko IChechulin A(2021)An Approach to Ranking the Sources of Information Dissemination in Social NetworksInformation10.3390/info1210041612:10(416)Online publication date: 11-Oct-2021
https://doi.org/10.3390/info12100416
Zeng YWang DZhu H(2016)User Interests Analysis and Its Application on the Linked Brain Data PlatformBrain Informatics and Health10.1007/978-3-319-47103-7_16(161-172)Online publication date: 23-Sep-2016
https://doi.org/10.1007/978-3-319-47103-7_16
Ninawe SVenkataram P(2015)Web Service Query Selection for a Professional Social Network Members2015 9th International Conference on Next Generation Mobile Applications, Services and Technologies10.1109/NGMAST.2015.60(29-34)Online publication date: Sep-2015
https://doi.org/10.1109/NGMAST.2015.60
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Index Terms

Ranking and combining social network data for web personalization
1. Information systems
  1. Information retrieval
    1. Users and interactive retrieval
      1. Personalization
  2. World Wide Web
    1. Web searching and information discovery
      1. Personalization

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Published In

DUBMMSM '12: Proceedings of the 2012 workshop on Data-driven user behavioral modelling and mining from social media

October 2012

46 pages

ISBN:9781450317078

DOI:10.1145/2390131

Program Chairs:
Jalal Mahmud
IBM Research -- Almaden
,
James Caverlee
Texas A&M University
,
Jeffrey Nichols
IBM Research -- Almaden
,
John O'Donovan
University of California, Santa Barbara
,
Michelle Zhou
IBM Research -- Almaden

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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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 29 October 2012

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CIKM'12

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CIKM'12: 21st ACM International Conference on Information and Knowledge Management

October 29, 2012

Hawaii, Maui, USA

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Overall Acceptance Rate 15 of 20 submissions, 75%

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CIKM '25

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The 34th ACM International Conference on Information and Knowledge Management

November 10 - 14, 2025

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Vitkova LKotenko IChechulin A(2021)An Approach to Ranking the Sources of Information Dissemination in Social NetworksInformation10.3390/info1210041612:10(416)Online publication date: 11-Oct-2021
https://doi.org/10.3390/info12100416
Zeng YWang DZhu H(2016)User Interests Analysis and Its Application on the Linked Brain Data PlatformBrain Informatics and Health10.1007/978-3-319-47103-7_16(161-172)Online publication date: 23-Sep-2016
https://doi.org/10.1007/978-3-319-47103-7_16
Ninawe SVenkataram P(2015)Web Service Query Selection for a Professional Social Network Members2015 9th International Conference on Next Generation Mobile Applications, Services and Technologies10.1109/NGMAST.2015.60(29-34)Online publication date: Sep-2015
https://doi.org/10.1109/NGMAST.2015.60
Su W(2014)Integrating and mining virtual communities across multiple Online Social Networks: Concepts, approaches and challenges2014 Fourth International Conference on Digital Information and Communication Technology and its Applications (DICTAP)10.1109/DICTAP.2014.6821682(199-204)Online publication date: May-2014
https://doi.org/10.1109/DICTAP.2014.6821682

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User interests driven web personalization based on multiple social networks

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