skip to main content
10.1145/2345396.2345540acmotherconferencesArticle/Chapter ViewAbstractPublication PagesacciciConference Proceedingsconference-collections
research-article

Ranking importance based information on the world wide web

Published: 03 August 2012 Publication History

Abstract

Identifying useful features for classification and forecast tasks from a ranked data is highly difficult and challenging. By ranking user popularity ratings from normalised area histograms, a method of feature selection for ranked data inspired from the law of vital few is proposed. We propose that the attributes that are most stable against the variations in classes have their usefulness in a forecasting task, while the attributes that are most unstable between inter-class samples but most stable within intra-class samples have their usefulness in classification tasks. The performance of the proposed method is demonstrated through a realistic example of web-content data from Yahoo! research repository: the user rating of web pages. The attributes in the data when ranked based on their importance in a year show distinct characteristics of performance in the tasks of popularity forecast and classification.

References

[1]
Resnick, P., Iacovou, N., Suchak, M., Bergstrom, P., and Riedl, J. 1994. GroupLens: An Open Architecture for Collaborative Filtering of Netnews, Internal Research Report, MIT Center for Coordination Science.
[2]
Albrecht, D. W., Zukerman, I., 2007. Introduction to the special issue on statistical and probabilistic methods for user modeling. User Modeling and User-Adapted Interaction 17(1-2):1--4.
[3]
Chiu, B. and Webb, G. 1998. Using decision trees for agent modeling: improving prediction performance. User Modeling and User-Adapted Interaction 8:131--152.
[4]
Billsus D, Pazzani JM 1998. Learning collaborative information filters. In: ICML '98: Proceedings of the fifteenth International Conference on Machine Learning, Morgan Kaufmann Publishers Inc., San Francisco, CA, USA, pp 46--54.
[5]
Billsus D, Pazzani JM 1999. A hybrid user model for news story classification. In: UM '99 Proceedings of the seventh international conference on User modelling, Springer-Verlag New York, Inc., Secaucus, NJ, USA, pp 99--108.
[6]
Breese JS, Heckerman D, Kadie C 1998. Empirical analysis of predictive algorithms for collaborative filtering. Morgan Kaufmann, pp 43--52.
[7]
Bueno RM, Pain H, Conlon T 2000. Understandable learner models for a sensor motor control task. In: ITS '00: Proceedings of the 5th International Conference on Intelligent Tutoring Systems, Springer-Verlag, London, UK, pp 222--231.
[8]
Holland J, 1975. Adaptation in Natural and Artificial Systems. The University of Michigan Press, Ann Arbor.
[9]
Jaffe W, 1972. Pareto translated: a review article. Journal of Economic Literature 10(4)
[10]
Joachims T, 1997. A probabilistic analysis of the rocchio algorithm with tfidf for text categorization. In: ICML '97: Proceedings of the Fourteenth International Conference on Machine Learning, Morgan Kaufmann Publishers Inc., San Francisco, CA, USA, pp 143--151
[11]
Jung SY, Hong J, Kim T, 2005. A statistical model for user preference. IEEE Trans on Knowledge and Data Engineering 7(6):834--843
[12]
Nakamura A, Abe N, 1998. Collaborative filtering using weighted majority prediction algorithms. In: ICML '98: Proceedings of the Fifteenth International Conference on Machine Learning, Morgan Kaufmann Publishers Inc., San Francisco, CA, USA, pp 395--403
[13]
Perkowitz M, Etzioni O, 2000. Towards adaptive web sites: conceptual framework and case study. Artificial Intelligence 118(1--2):245--275
[14]
Schwab I, Pohl W, 1999. Learning user profiles from positive examples. http://labsrepos.iit.demokritos.gr/skel/eetn/acai99/Workshops/w03/w03_02.pdf.gz, pp 15--20
[15]
Zhang T, Oles FJ, 2001. Text categorization based on regularized linear classification methods. Information Retrieval 4(1):5--31
[16]
Guyon I, Elisseeff A, 2003. An introduction to variable and feature selection. Journal of Machine Learning Research 3 1157--1182
[17]
Lei Y, Chris D, and Steven L. 2008. Stable feature selection via dense feature groups. In Proceedings of the 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
[18]
Kan M and Thi H. 2005. Fast webpage classification using URL features. Proceedings of the 14th ACM international conference on Information and knowledge management. pp 325--326

Recommendations

Comments

Information & Contributors

Information

Published In

cover image ACM Other conferences
ICACCI '12: Proceedings of the International Conference on Advances in Computing, Communications and Informatics
August 2012
1307 pages
ISBN:9781450311960
DOI:10.1145/2345396
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]

Sponsors

  • ISCA: International Society for Computers and Their Applications
  • RPS: Research Publishing Services

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 03 August 2012

Permissions

Request permissions for this article.

Check for updates

Author Tags

  1. WWW
  2. feature selection
  3. ranked distribution
  4. user rating

Qualifiers

  • Research-article

Conference

ICACCI '12
Sponsor:
  • ISCA
  • RPS

Contributors

Other Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

  • 0
    Total Citations
  • 103
    Total Downloads
  • Downloads (Last 12 months)1
  • Downloads (Last 6 weeks)0
Reflects downloads up to 20 Feb 2025

Other Metrics

Citations

View Options

Login options

View options

PDF

View or Download as a PDF file.

PDF

eReader

View online with eReader.

eReader

Figures

Tables

Media

Share

Share

Share this Publication link

Share on social media