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Ranking user-created contents by search user's inclination in online communities

Authors:

Hyoseop Shin,

Jeehoon LeeAuthors Info & Claims

WWW '09: Proceedings of the 18th international conference on World wide web

Pages 1215 - 1216

https://doi.org/10.1145/1526709.1526935

Published: 20 April 2009 Publication History

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Abstract

Searching posts effectively has become an important issue in large-scale online communities. Especially, if search users have different inclinations when they search posts, they have different kinds of posts in their minds. To address this problem, in this paper, we propose a scheme of ranking posts based on search users' inclination. User ranking score is employed to capture posts that are relevant to a specific user inclination. Specifically, we present a scheme to rank posts in terms of user expertise and popularity. Experimental results show that different user inclinations can produce quite different search results and the proposed scheme achieves about 70% accuracy.

References

[1]

Hyoseop Shin, Zhiwei Xu, and Eun Yi Kim, "Discovering and Browsing of Power Users by Social Relationship Analysis in Large-scale Online Communities," Proc. of 2008 IEEE/WIC/ACM conf. on Web Intelligence, Sydney, Australia, Dec. 2008.

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[2]

Expertise/Popularity-based Community Search Demo, "http://webi.konkuk.ac.kr/ep_rank".

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[3]

J. Luxenburger, S. Elbassuoni, and G. Weikum, "Matching task profiles and user needs in personalized web search," Proc. of 2008 ACM CIKM conference, pages 689--698, Napa Valley, USA, Sep. 2008.

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Cited By

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Speily ORezvanian AGhasemzadeh ASaghiri AVahidipour S(2019)Lurkers Versus Posters: Investigation of the Participation Behaviors in Online Learning CommunitiesEducational Networking10.1007/978-3-030-29973-6_8(269-298)Online publication date: 9-Nov-2019
https://doi.org/10.1007/978-3-030-29973-6_8
Shin HLee J(2012)Impact and degree of user sociability in social mediaInformation Sciences: an International Journal10.1016/j.ins.2012.01.040196(28-46)Online publication date: 1-Aug-2012
https://dl.acm.org/doi/10.1016/j.ins.2012.01.040
Shin HLee JHwang KShin SOssowski SSchumacher MPalakal MHung C(2010)Separating the reputation and the sociability of online community usersProceedings of the 2010 ACM Symposium on Applied Computing10.1145/1774088.1774470(1807-1814)Online publication date: 22-Mar-2010
https://dl.acm.org/doi/10.1145/1774088.1774470

Index Terms

Ranking user-created contents by search user's inclination in online communities
1. Information systems
  1. Information retrieval
    1. Retrieval tasks and goals
      1. Document filtering
      2. Information extraction

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Information & Contributors

Information

Published In

WWW '09: Proceedings of the 18th international conference on World wide web

April 2009

1280 pages

ISBN:9781605584874

DOI:10.1145/1526709

General Chairs:
Juan Quemada
DIT-UPM
,
Gonzalo León
DIT-UPM
,
Program Chairs:
Yoelle Maarek
Google Inc., Israel
,
Wolfgang Nejdl
L3S and Hannover University

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 20 April 2009

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WWW '09

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WWW '09: The 18th International World Wide Web Conference

April 20 - 24, 2009

Madrid, Spain

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

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Cited By

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Speily ORezvanian AGhasemzadeh ASaghiri AVahidipour S(2019)Lurkers Versus Posters: Investigation of the Participation Behaviors in Online Learning CommunitiesEducational Networking10.1007/978-3-030-29973-6_8(269-298)Online publication date: 9-Nov-2019
https://doi.org/10.1007/978-3-030-29973-6_8
Shin HLee J(2012)Impact and degree of user sociability in social mediaInformation Sciences: an International Journal10.1016/j.ins.2012.01.040196(28-46)Online publication date: 1-Aug-2012
https://dl.acm.org/doi/10.1016/j.ins.2012.01.040
Shin HLee JHwang KShin SOssowski SSchumacher MPalakal MHung C(2010)Separating the reputation and the sociability of online community usersProceedings of the 2010 ACM Symposium on Applied Computing10.1145/1774088.1774470(1807-1814)Online publication date: 22-Mar-2010
https://dl.acm.org/doi/10.1145/1774088.1774470

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Online learning for recency search ranking using real-time user feedback

Search Engine Optimization by Re-Ranking the Product Search Result Based on User Click Data

User behavior modeling for better Web search ranking