short-paper

Using twitter to recommend real-time topical news

Authors:

Owen Phelan,

Kevin McCarthy,

Barry SmythAuthors Info & Claims

RecSys '09: Proceedings of the third ACM conference on Recommender systems

Pages 385 - 388

https://doi.org/10.1145/1639714.1639794

Published: 23 October 2009 Publication History

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Abstract

Recommending news stories to users, based on their preferences, has long been a favourite domain for recommender systems research. In this paper, we describe a novel approach to news recommendation that harnesses real-time micro-blogging activity, from a service such as Twitter, as the basis for promoting news stories from a user's favourite RSS feeds. A preliminary evaluation is carried out on an implementation of this technique that shows promising results.

References

[1]

Daniel Billsus and Michael J. Pazzani. A personal news agent that talks, learns and explains. In In Proceedings of the Third International Conference on Autonomous Agents, pages 268--275. ACM Press, 1999.

Digital Library

Google Scholar

[2]

Akshay Java, Xiaodan Song, Tim Finin, and Belle Tseng. Why we twitter: understanding microblogging usage and communities. In WebKDD/SNA-KDD '07: Proceedings of the 9th WebKDD and 1st SNA-KDD 2007 workshop on Web mining and social network analysis, pages 56--65, NY, USA, 2007. ACM.

Digital Library

Google Scholar

[3]

Tomonari Kamba, Krishna Bharat, and Michael C. Albers. The krakatoa chronicle -- an interactive, personalized, newspaper on the web. In Proceedings of the Fourth International World Wide Web Conference, pages 159--170, 1995.

Google Scholar

[4]

Balachander Krishnamurthy, Phillipa Gill, and Martin Arlitt. A few chirps about twitter. In WOSP '08: Proceedings of the First workshop on Online social networks, pages 19--24, NY, USA, 2008. ACM.

Digital Library

Google Scholar

[5]

Ken Lang. Newsweeder: learning to filter netnews. In Proceedings of the 12th International Conference on Machine Learning, pages 331--339. Morgan Kaufmann publishers Inc.: San Mateo, CA, USA, 1995.

Digital Library

Google Scholar

[6]

Michael Pazzani and Daniel Billsus. Content-based recommendation systems. The Adaptive Web, pages 325--341, 2007.

Digital Library

Google Scholar

[7]

G. Salton and M. J. Mcgill. Introduction to Modern Information Retrieval. McGraw-Hill, Inc., New York, NY, USA, 1986.

Digital Library

Google Scholar

[8]

B. Smyth and P. Cotter. A personalised tv listings service for the digital tv age. Knowledge-Based Systems, 13(2--3):53 -- 59, 2000.

Google Scholar

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Zhang BDoroshenko VKairouz PSteinke TThakurta AMa ZCohen EApte HSpacek J(2024)Differentially Private Stream Processing at ScaleProceedings of the VLDB Endowment10.14778/3685800.368583317:12(4145-4158)Online publication date: 1-Aug-2024
https://dl.acm.org/doi/10.14778/3685800.3685833
Zare GJafari Navimipour NHosseinzadeh MSahafi A(2024)Network link prediction via deep learning method: A comparative analysis with traditional methodsEngineering Science and Technology, an International Journal10.1016/j.jestch.2024.10178256(101782)Online publication date: Aug-2024
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Index Terms

Using twitter to recommend real-time topical news
1. Information systems
  1. Information systems applications

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Information & Contributors

Information

Published In

RecSys '09: Proceedings of the third ACM conference on Recommender systems

October 2009

442 pages

ISBN:9781605584355

DOI:10.1145/1639714

General Chairs:
Lawrence Bergman
IBM Research, NY, USA
,
Alex Tuzhilin
New York University, NY, USA
,
Program Chairs:
Robin Burke
DePaul University, USA
,
Alexander Felfernig
Graz University of Technology, Austria
,
Lars Schmidt-Thieme
University of Hildesheim, Germany

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: 23 October 2009

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RecSys '09

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SIGCHI

RecSys '09: Third ACM Conference on Recommender Systems

October 23 - 25, 2009

New York, New York, USA

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Overall Acceptance Rate 254 of 1,295 submissions, 20%

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Cited By

View all

Zhang BDoroshenko VKairouz PSteinke TThakurta AMa ZCohen EApte HSpacek J(2024)Differentially Private Stream Processing at ScaleProceedings of the VLDB Endowment10.14778/3685800.368583317:12(4145-4158)Online publication date: 1-Aug-2024
https://dl.acm.org/doi/10.14778/3685800.3685833
Zare GJafari Navimipour NHosseinzadeh MSahafi A(2024)Network link prediction via deep learning method: A comparative analysis with traditional methodsEngineering Science and Technology, an International Journal10.1016/j.jestch.2024.10178256(101782)Online publication date: Aug-2024
https://doi.org/10.1016/j.jestch.2024.101782
Ding YWang BCui XXu M(2024)Popularity prediction with semantic retrieval for news recommendationExpert Systems with Applications10.1016/j.eswa.2024.123308(123308)Online publication date: Feb-2024
https://doi.org/10.1016/j.eswa.2024.123308
Li XLi RPeng QYao J(2024)Graph neural news recommendation based on multi-view representation learningThe Journal of Supercomputing10.1007/s11227-024-06025-9Online publication date: 20-Mar-2024
https://doi.org/10.1007/s11227-024-06025-9
Yong HHwang H(2023)Mining Twitter Texts for the COVID-19 Pandemic in KoreaJournal of Digital Contents Society10.9728/dcs.2023.24.5.111124:5(1111-1120)Online publication date: 31-May-2023
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https://doi.org/10.3390/info14020060
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Yang ZCui LWang XZhang TCheng Y(2023)MIAR: Interest-Activated News Recommendation by Fusing Multichannel InformationIEEE Transactions on Computational Social Systems10.1109/TCSS.2022.320194410:6(3433-3443)Online publication date: Dec-2023
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