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User comments for news recommendation in social media

Published: 19 July 2010 Publication History

Abstract

Reading and Commenting online news is becoming a common user behavior in social media. Discussion in the form of comments following news postings can be effectively facilitated if the service provider can recommend articles based on not only the original news itself but also the thread of changing comments. This turns the traditional news recommendation to a "discussion moderator" that can intelligently assist online forums. In this work, we present a framework to recommend relevant information in the forum-based social media using user comments. When incorporating user comments, we consider structural and semantic information carried by them. Experiments indicate that our proposed solutions provide an effective recommendation service.

References

[1]
T. Bogers and A. Bosch. Comparing and evaluating information retrieval algorithms for news recommendation. In Proc. of ACM Recommender systems, 2007.
[2]
S. Brin and L. Page. The anatomy of a large-scale hypertextual web search engine. Computer networks and ISDN systems, 30(1--7):107--117, 1998.
[3]
J.-H. Chiang and Y.-C. Chen. An intelligent news recommender agent for filtering and categorizing large volumes of text corpus. International Journal of Intelligent Systems, 19(3):201--216, 2004.
[4]
V. Lavrenko, M. Schmill, D. Lawrie, P. Ogilvie, D. Jensen, and J. Allan. Language models for financial news recommendation. In Proc. of CIKM, 2000.

Cited By

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  • (2024)Where Are the Values? A Systematic Literature Review on News Recommender SystemsACM Transactions on Recommender Systems10.1145/36548052:3(1-40)Online publication date: 28-Mar-2024
  • (2024)Developing Custom-Made Comment-Recommendation Prototypes with a Modular Design FrameworkSocial Computing and Social Media10.1007/978-3-031-61281-7_7(97-112)Online publication date: 1-Jun-2024
  • (2022)Social Media Recommender Systems (SMRS): A Bibliometric Analysis Study 2000–2021IEEE Access10.1109/ACCESS.2022.316149710(35479-35497)Online publication date: 2022
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Published In

cover image ACM Conferences
SIGIR '10: Proceedings of the 33rd international ACM SIGIR conference on Research and development in information retrieval
July 2010
944 pages
ISBN:9781450301534
DOI:10.1145/1835449
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.

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 19 July 2010

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Author Tags

  1. comments
  2. information filtering
  3. news recommendation
  4. social media

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SIGIR '10
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SIGIR '10 Paper Acceptance Rate 87 of 520 submissions, 17%;
Overall Acceptance Rate 792 of 3,983 submissions, 20%

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Cited By

View all
  • (2024)Where Are the Values? A Systematic Literature Review on News Recommender SystemsACM Transactions on Recommender Systems10.1145/36548052:3(1-40)Online publication date: 28-Mar-2024
  • (2024)Developing Custom-Made Comment-Recommendation Prototypes with a Modular Design FrameworkSocial Computing and Social Media10.1007/978-3-031-61281-7_7(97-112)Online publication date: 1-Jun-2024
  • (2022)Social Media Recommender Systems (SMRS): A Bibliometric Analysis Study 2000–2021IEEE Access10.1109/ACCESS.2022.316149710(35479-35497)Online publication date: 2022
  • (2016)Efficient Promotion Algorithm by Exploring Group Preference in Recommendation2016 IEEE International Conference on Web Services (ICWS)10.1109/ICWS.2016.42(268-275)Online publication date: Jun-2016
  • (2015)Implicit Feedback Mining for RecommendationBig Data Computing and Communications10.1007/978-3-319-22047-5_30(373-385)Online publication date: 24-Jul-2015
  • (2014)Combining Semantics and Social Knowledge for News Article SummarizationData Mining and Analysis in the Engineering Field10.4018/978-1-4666-6086-1.ch012(209-230)Online publication date: 2014
  • (2013)News Document Summarization Driven by User-Generated ContentSocial Media Mining and Social Network Analysis10.4018/978-1-4666-2806-9.ch007(105-126)Online publication date: 2013
  • (2013)Topical organization of user comments and application to content recommendationProceedings of the 22nd International Conference on World Wide Web10.1145/2487788.2487812(61-62)Online publication date: 13-May-2013
  • (2012)Analyzing Twitter User Behaviors and Topic Trends by Exploiting Dynamic RulesBehavior Computing10.1007/978-1-4471-2969-1_17(267-287)Online publication date: 2012
  • (2011)Personalized Book Recommendations Created by Using Social Media DataWeb Information Systems Engineering – WISE 2010 Workshops10.1007/978-3-642-24396-7_31(390-403)Online publication date: 2011
  • Show More Cited By

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