Article

Comparing and evaluating information retrieval algorithms for news recommendation

Authors:

Antal van den BoschAuthors Info & Claims

RecSys '07: Proceedings of the 2007 ACM conference on Recommender systems

Pages 141 - 144

https://doi.org/10.1145/1297231.1297256

Published: 19 October 2007 Publication History

Abstract

In this paper, we argue that the performance of content-based news recommender systems has been hampered by using relatively old and simple matching algorithms. Using more current probabilistic retrieval algorithms results in significant performance boosts. We test our ideas on a test collection that we have made publicly available. We perform both binary and graded evaluation of our algorithms and argue for the need for more graded evaluation of content-based recommender systems.

References

[1]

P. N. Bennett, S. T. Dumais, and E. Horvitz. The Combination of Text Classifiers Using Reliability Indicators. Information Retrieval, 8(1):67--100, 2005.

Digital Library

[2]

D. Billsus and M. J. Pazzani. A personal news agent that talks, learns and explains. In O. Etzioni, J. P. Müller, and J. M. Bradshaw, editors, Proceedings of Agents '99, pages 268--275, New York, NY, 1999. ACM Press.

Digital Library

[3]

T. Brants and R. Stolle. Finding similar documents in document collections. In Proceedings of LREC-2002, Workshop on Using Semantics for Information Retrieval and Filtering, 2002.

[4]

J. P. Callan. Passage-level evidence in document retrieval. In W. B. Croft and C. van Rijsbergen, editors, Proceedings of SIGIR '94, pages 302--310, New York, NY, USA, July 1994. Springer Verlag.

Digital Library

[5]

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, 19:201--216, 2004.

Digital Library

[6]

M. Claypool, A. Gokhale, T. Miranda, P. Murnikov, D. Netes, and M. Sartin. Combining content-based and collaborative filters in an online newspaper. In Proceedings of the SIGIR Workshop on Recommender Systems, August 1999.

[7]

W. J. Conover. Practical Non-Parametric Statistics. John Wiley and Sons, New York, NY, second edition, 1980.

[8]

A. Das, M. Datar, A. Garg, and S. Rajaram. Google News Personalization: Scalable Online Collaborative Filtering. In Proceedings of WWW '07, pages 271--280, 2007.

Digital Library

[9]

W. Fox. Writing the News: A Guide for Print Journalists. Iowa State University Press, second edition, 2001.

[10]

S. H. Ha. Digital Content Recommender on the Internet. IEEE Intelligent Systems, 21(2):70--77, 2006.

Digital Library

[11]

V. Lavrenko, M. Schmill, D. Lawrie, P. Ogilvie, D. Jensen, and J. Allan. Language models for financial news recommendation. In Proceedings of CIKM 2000, pages 389--396, New York, NY, 2000. ACM Press.

Digital Library

[12]

M. Montaner, B. López, and J. L. de la Rosa. A Taxonomy of Recommender Agents on the Internet. Artificial Intelligence Review, 19(4):285--330, 2003.

Digital Library

[13]

R. J. Mooney and L. Roy.Content-based book recommending using learning for text categorization. In Proceedings of DL '00, pages 195--204, New York, NY, 2000. ACM Press.

Digital Library

[14]

J. M. Ponte and W. B. Croft. A language modeling approach to information retrieval. In Proceedings of SIGIR '99, pages 275--281, New York, NY, 1998. ACM Press.

Digital Library

[15]

S. Robertson and S. Walker. Some simple effective approximations to the 2-poisson model for probabilistic weighted retrieval. In Proceedings of SIGIR '94, pages 232--241, New York, NY, 1994. ACM Press.

Digital Library

[16]

M. Sanderson. Reuters test collection. Technical report, Glasgow University Comp. Sc. Department, June 1994.

[17]

J. Sigmund. Online Newspaper Audience Sets Records in First Quarter, April 2007. Last visited: June 2007, http://www.naa.org.

[18]

A. Singhal, G. Salton, M. Mitra, and C. Buckley. Document length normalization. Information Processing and Management, 32(5):619--633, 1996.

Digital Library

[19]

N. Tintarev and J. Masthoff. Similarity for news recommender systems. In Proceedings of the AH'06 Workshop on Recommender Systems and Intelligent User Interfaces, 2006.

[20]

E. M. Voorhees and D. Harman, editors. TREC: Experiment and Evaluation in Information Retrieval. MIT Press, Cambridge, MA, 2005.

Digital Library

Cited By

Iana AAlam MPaulheim H(2024)A survey on knowledge-aware news recommender systemsSemantic Web10.3233/SW-22299115:1(21-82)Online publication date: 12-Jan-2024
https://doi.org/10.3233/SW-222991
Vercoutere SJoris GDe Pessemier TMartens L(2024)Improving selection diversity using hybrid graph-based news recommendersUser Modeling and User-Adapted Interaction10.1007/s11257-024-09399-wOnline publication date: 12-Jun-2024
https://doi.org/10.1007/s11257-024-09399-w
Wu CWu FHuang YXie X(2023)Personalized News Recommendation: Methods and ChallengesACM Transactions on Information Systems10.1145/353025741:1(1-50)Online publication date: 10-Jan-2023
https://dl.acm.org/doi/10.1145/3530257
Show More Cited By

Index Terms

Comparing and evaluating information retrieval algorithms for news recommendation
1. Information systems
  1. Information retrieval
    1. Evaluation of retrieval results

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Published In

cover image ACM Conferences

RecSys '07: Proceedings of the 2007 ACM conference on Recommender systems

October 2007

222 pages

ISBN:9781595937308

DOI:10.1145/1297231

General Chair:
Joseph A. Konstan
University of Minnesota, USA
,
Program Chairs:
John Riedl
University of Minnesota, USA
,
Barry Smyth
University College, Dublin, Ireland

Copyright © 2007 ACM.

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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Publication History

Published: 19 October 2007

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RecSys07

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RecSys07: ACM Conference on Recommender Systems

October 19 - 20, 2007

MN, Minneapolis, USA

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

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RecSys '24

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18th ACM Conference on Recommender Systems

October 14 - 18, 2024

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Cited By

Iana AAlam MPaulheim H(2024)A survey on knowledge-aware news recommender systemsSemantic Web10.3233/SW-22299115:1(21-82)Online publication date: 12-Jan-2024
https://doi.org/10.3233/SW-222991
Vercoutere SJoris GDe Pessemier TMartens L(2024)Improving selection diversity using hybrid graph-based news recommendersUser Modeling and User-Adapted Interaction10.1007/s11257-024-09399-wOnline publication date: 12-Jun-2024
https://doi.org/10.1007/s11257-024-09399-w
Wu CWu FHuang YXie X(2023)Personalized News Recommendation: Methods and ChallengesACM Transactions on Information Systems10.1145/353025741:1(1-50)Online publication date: 10-Jan-2023
https://dl.acm.org/doi/10.1145/3530257
Shakil Ubaid SAlam MSohail SSaifi IMufti TAziz ANafis M(2021)The Impact of Randomized Algorithm over Recommender SystemProcedia Computer Science10.1016/j.procs.2021.10.076194(218-223)Online publication date: 2021
https://doi.org/10.1016/j.procs.2021.10.076
Alam MUbaid SShakil Sohail SNadeem MHussain SSiddiqui J(2021)Comparative Analysis of Machine Learning based Filtering Techniques using MovieLens datasetProcedia Computer Science10.1016/j.procs.2021.10.075194(210-217)Online publication date: 2021
https://doi.org/10.1016/j.procs.2021.10.075
Belavadi PBurbach LAhlers SZiefle MCalero Valdez A(2021)Expectation, Perception, and Accuracy in News Recommender Systems: Understanding the Relationships of User Evaluation Criteria Using Direct FeedbackHCI International 2021 - Late Breaking Papers: Design and User Experience10.1007/978-3-030-90238-4_14(179-197)Online publication date: 20-Nov-2021
https://doi.org/10.1007/978-3-030-90238-4_14
Borovič MFerme MBrezovnik JMajninger SKac KOjsteršek M(2020)Document Recommendations and Feedback Collection Analysis within the Slovenian Open-Access InfrastructureInformation10.3390/info1111049711:11(497)Online publication date: 23-Oct-2020
https://doi.org/10.3390/info11110497
Feng CKhan MRahman AAhmad A(2020)News Recommendation Systems - Accomplishments, Challenges & Future DirectionsIEEE Access10.1109/ACCESS.2020.29677928(16702-16725)Online publication date: 2020
https://doi.org/10.1109/ACCESS.2020.2967792
Veloso BAssunção RFerreira AZiviani N(2019)In Search of a Stochastic Model for the E-News ReaderACM Transactions on Knowledge Discovery from Data10.1145/336269513:6(1-27)Online publication date: 13-Nov-2019
https://dl.acm.org/doi/10.1145/3362695
Catena MFrieder OMuntean CNardini FPerego RTonellotto NPiwowarski BChevalier MGaussier EMaarek YNie JScholer F(2019)Enhanced News RetrievalProceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3331184.3331373(1269-1272)Online publication date: 18-Jul-2019
https://dl.acm.org/doi/10.1145/3331184.3331373
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