research-article

Online selection of parameters in the rocchio algorithm for identifying interesting news articles

Authors:

Raymond K. Pon,

Alfonso F. Cárdenas,

David J. ButtlerAuthors Info & Claims

WIDM '08: Proceedings of the 10th ACM workshop on Web information and data management

Pages 141 - 148

https://doi.org/10.1145/1458502.1458525

Published: 30 October 2008 Publication History

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Abstract

We show that users have different reading behavior when evaluating the interestingness of articles, calling for different parameter configurations for information retrieval algorithms for different users. Better recommendation results can be made if parameters for common information retrieval algorithms, such as the Rocchio algorithm, are learned dynamically instead of being statically fixed a priori. By dynamically learning good parameter configurations, Rocchio can adapt to differences in user behavior among users. We show that by adaptively learning online the parameters of a simple retrieval algorithm, similar recommendation performance can be achieved as more complex algorithms or algorithms that require extensive fine-tuning. Also we have also shon that online parameter learning can yield 10% better results than best performing filter from the TREC11 adaptive filter task.

References

[1]

J. Wang, A. P. de Vries, and M. J. T. Reinders, "Unifying user-based and item-based collaborative filtering approaches by similarity fusion," in Proceedings of 29th annual Intl. ACM SIGIR Conf. on Research and development in information retrieval, 2006.

Digital Library

Google Scholar

[2]

R. K. Pon, A. F. Cardenas, D. Buttler, and T. Critchlow, "iScore: Measuring the interestingness of articles in a limited user environment," in IEEE Symposium on Computational Intelligence and Data Mining 2007, (Honolulu, HI), April 2007.

Google Scholar

[3]

J. Rocchio, Relevance Feedback in Information Retrieval, ch. 14, pp. 313--323. Prentice-Hall, 1971.

Google Scholar

[4]

H. Xu, Z. Yang, B. Wang, B. Liu, J. Cheng, Y. Liu, Z. Yang, X. Cheng, and S. Bai, "TREC-11 experiments at CAS-ICT: Filtering and web," in TREC11, 2002

Google Scholar

[5]

R. E. Schapire, Y. Singer, and A. Singhal, "Boosting and rocchio applied to text filtering," in Proceedings of SIGIR-98, 21st ACM International Conference on Research and Development in Information Retrieval (W. B. Croft, A. Moffat, C. J. van Rijsbergen, R. Wilkinson, and J. Zobel, eds.), (Melbourne, AU), pp. 215--223, ACM Press, New York, US, 1998.

Digital Library

Google Scholar

[6]

A. Singhal, M. Mitra, and C. Buckley, "Learning routing queries in a query zone," in Proceedings of the Twentieth Annual Internal ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 25--32, July 1997.

Digital Library

Google Scholar

[7]

R. K. Pon, A. F. Cardenas, D. Buttler, and T. Critchlow, "Tracking multiple topics for finding interesting articles," in KDD '07: Proceedings of the 13th ACM SIGKDD international conference on knowledge discovery and data mining, (New York, NY, USA), pp. 560--569, ACM Press, 2007.

Digital Library

Google Scholar

[8]

Z. Chen and B. Zhu, "Some formal analysis of Rocchio's similarity-based relevance feedback algorithm," Inf. Retr., vol. 5, no. 1, pp. 61--86, 2002.

Digital Library

Google Scholar

[9]

T. Joachims, "A probabilistic analysis of the Rocchio algorithm with TF-IDF for text categorization," Tech. Rep. CMU-CS-96-118, Carnegie Mellon University, 1996.

Google Scholar

[10]

S. Robertson and I. Soboroff, "The TREC 2002 filtering track report," in TREC 2002, 2002.

Google Scholar

[11]

Apache, "Apache UIMA." {Online} http://incubator.apache.org/uima/, 2008.

Google Scholar

[12]

R.K. Pon, "iScore." {Online} http://sourceforge.net/projects/iscore/, 2008.

Google Scholar

[13]

Yahoo, "Yahoo news RSS feeds." {Online} http://news.yahoo.com/rss, 2008.

Google Scholar

[14]

Alias-I, "LingPipe," {Online}. http://www.alias-i.com/lingpipe/index.html, 2008.

Google Scholar

Cited By

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Bauer CBagchi CHundogan Ovan Es K(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
https://dl.acm.org/doi/10.1145/3654805
Azzopardi JIvanovic DKapitsaki G(2017)Comparison of Collaborative and Content-Based Automatic Recommendation Approaches in a Digital Library of Serbian PhD DissertationsSemantic Keyword-Based Search on Structured Data Sources10.1007/978-3-319-53640-8_9(100-111)Online publication date: 15-Feb-2017
https://doi.org/10.1007/978-3-319-53640-8_9
Wang CShen YYang HGuo M(2013)Improving Rocchio Algorithm for Updating User Profile in Recommender SystemsWeb Information Systems Engineering – WISE 201310.1007/978-3-642-41230-1_14(162-174)Online publication date: 2013
https://doi.org/10.1007/978-3-642-41230-1_14
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Index Terms

Online selection of parameters in the rocchio algorithm for identifying interesting news articles
1. Information systems
  1. Information retrieval
    1. Document representation

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Published In

WIDM '08: Proceedings of the 10th ACM workshop on Web information and data management

October 2008

164 pages

ISBN:9781605582603

DOI:10.1145/1458502

Program Chairs:
Chee-Yong Chan
National University of Singapore, Singapore
,
Neoklis Polyzotis
University of California-Santa Cruz, USA

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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Published: 30 October 2008

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October 30, 2008

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Bauer CBagchi CHundogan Ovan Es K(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
https://dl.acm.org/doi/10.1145/3654805
Azzopardi JIvanovic DKapitsaki G(2017)Comparison of Collaborative and Content-Based Automatic Recommendation Approaches in a Digital Library of Serbian PhD DissertationsSemantic Keyword-Based Search on Structured Data Sources10.1007/978-3-319-53640-8_9(100-111)Online publication date: 15-Feb-2017
https://doi.org/10.1007/978-3-319-53640-8_9
Wang CShen YYang HGuo M(2013)Improving Rocchio Algorithm for Updating User Profile in Recommender SystemsWeb Information Systems Engineering – WISE 201310.1007/978-3-642-41230-1_14(162-174)Online publication date: 2013
https://doi.org/10.1007/978-3-642-41230-1_14
Azzopardi JStaff C(2012)Automatic Adaptation and Recommendation of News Reports Using Surface-Based MethodsHighlights on Practical Applications of Agents and Multi-Agent Systems10.1007/978-3-642-28762-6_9(69-76)Online publication date: 2012
https://doi.org/10.1007/978-3-642-28762-6_9
Pon RCárdenas AButtler DCritchlow T(2011)Measuring the interestingness of articles in a limited user environmentInformation Processing and Management: an International Journal10.1016/j.ipm.2010.03.00147:1(97-116)Online publication date: 1-Jan-2011
https://dl.acm.org/doi/10.1016/j.ipm.2010.03.001
Nanas NVavalis MDe Roeck A(2010)Words, antibodies and their interactionsSwarm Intelligence10.1007/s11721-010-0044-64:4(275-300)Online publication date: 27-Aug-2010
https://doi.org/10.1007/s11721-010-0044-6
Nanas NRoeck AVavalis M(2009)What Happened to Content-Based Information Filtering?Proceedings of the 2nd International Conference on Theory of Information Retrieval: Advances in Information Retrieval Theory10.1007/978-3-642-04417-5_23(249-256)Online publication date: 3-Sep-2009
https://dl.acm.org/doi/10.1007/978-3-642-04417-5_23

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Measuring the interestingness of articles in a limited user environment

Tracking multiple topics for finding interesting articles

Modeling and broadening temporal user interest in personalized news recommendation

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