Skip to main content
SpringerLink
Log in
Book cover

Transactions on Rough Sets XIV pp 18–36Cite as

High Scent Web Page Recommendations Using Fuzzy Rough Set Attribute Reduction

  • Conference paper

  • 553 Accesses

Part of the book series: Lecture Notes in Computer Science ((TRS,volume 6600))

Abstract

Information on the web is huge in size and to find the relevant information according to the information need of the user is a big challenge. Information scent of the clicked pages of the past query sessions has been used in the literature to generate web page recommendations for satisfying the information need of the current user. High scent information retrieval works on the bedrock of keyword vector of query sessions clustered using information scent. The dimensionality of the keyword vector is very high which affects the classification accuracy and computational efficiency associated with the processing of input queries and ultimately affects the precision of information retrieval. All the keywords in the keyword vector are not equally important for identifying the varied and differing information needs represented by clusters. Fuzzy Rough Set Attribute Reduction (FRSAR) has been applied in the presented work to reduce the high dimensionality of the keyword vector to obtain reduced relevant keywords resulting in improvement in space and time complexities. The effectiveness of fuzzy rough approach for high scent web page recommendations in information retrieval is verified with the experimental study conducted on the data extracted from the web history of Google search engine.

This is a preview of subscription content, log in via an institution.

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   39.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Learn about institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Allan, J.: Incremental relevance feedback for information filtering. In: Proceedings of the 19th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 270–278 (1996)

    Google Scholar 

  2. Baeza-Yates, R., Ribeiro-Neto, B.: Modern Information Retrieval. Addison-Wesley, Reading (1999)

    Google Scholar 

  3. Baeza-Yates, R., Hurtado, C.A., Mendoza, M.: Query recommendation using query logs in search engines. In: Favela, J., Menasalvas, E., Chávez, E. (eds.) AWIC 2004. LNCS (LNAI), vol. 3034, pp. 164–175. Springer, Heidelberg (2004)

    Chapter  Google Scholar 

  4. Bazan, J., Peters, J.F., Skowron, A., Nguyen, H.S., Szczuka, M.: Rough set approach for pattern extraction from classifiers. Electronics Notes in Theoretical Computer Science 82(4), 20–29 (2003)

    Article  MATH  Google Scholar 

  5. Bedi, P., Chawla, S.: Improving information retrieval precision using query log mining and information scent. Information Technology Journal 6(4), 584–588 (2007)

    Article  Google Scholar 

  6. Bedi, P., Chawla, S.: Use of fuzzy rough set attribute reduction in high scent web page recommendations. In: Sakai, H., Chakraborty, M.K., Hassanien, A.E., Ślęzak, D., Zhu, W. (eds.) RSFDGrC 2009. LNCS, vol. 5908, pp. 192–200. Springer, Heidelberg (2009)

    Chapter  Google Scholar 

  7. Chawla, S., Bedi, P.: Improving information retrieval precision by finding related queries with similar information need using information scent. In: Proceedings ICETET 2008. The 1st International Conference on Emerging Trends in Engineering and Technology, pp. 486–491 (2008)

    Google Scholar 

  8. Chawla, S., Bedi, P.: Personalized web search using information scent. In: Proceedings CISSE 2007 - International Joint Conferences on Computer, Information and Systems Science and Engineering, Springer, Heidelberg (2007)

    Google Scholar 

  9. Chawla, S., Bedi, P.: Finding hubs and authorities using information scent to improve the information retrieval precision. In: Proceedings ICAI 2008 -The 2008 International Conference on Artificial Intelligence, WORLDCOMP 2008, July 14-17 (2008)

    Google Scholar 

  10. Chi, E.H., Pirolli, P., Chen, K., Pitkow, J.: Using information scent to model user information needs and actions on the Web. In: Proceedings ACM CHI 2001 Conference on Human Factors in Computing Systems, pp. 490–497 (2001)

    Google Scholar 

  11. Chouchoulas, A., Shen, Q.: Rough set aided keyword reduction for text categorization. Applied Artificial Intelligence 15(9), 843–873 (2001)

    Article  Google Scholar 

  12. Cornelis, C., Jensen, R., Hurtado, G., Ślęzak, D.: Attribute selection with fuzzy decision reducts. Information Sciences 180, 209–224 (2010)

    Article  MathSciNet  MATH  Google Scholar 

  13. Cui, H., Wen, J.R., Nie, J.Y., Ma, W.Y.: Query expansion by mining user logs. IEEE Transactions on Knowledge and Data Engineering 15(4), 829–839 (2003)

    Article  Google Scholar 

  14. Duan, Q., Miao, D., Zhang, H., Zheng, J.: Personalized web retrieval based on rough fuzzy method. Journal of Computational Information Systems 3(2), 203–208 (2007)

    Google Scholar 

  15. Efron, M.: Query expansion and dimensionality reduction: Notions of optimality in Rocchio relevance feedback and latent semantic indexing. Information Processing and Management 44, 163–180 (2008)

    Article  Google Scholar 

  16. Gordon, A.D.: How many clusters? An investigation of five procedures for detecting nested cluster structure. In: Hayashi, C., Ohsumi, N., Yajima, K., Tanaka, Y., Bock, H., Baba, Y. (eds.) Data Science, Classification, and Related Methods, Springer, Tokyo (1998)

    Google Scholar 

  17. Gudivada, V.N., Raghavan, V.V., Grosky, W., Kasanagottu, R.: Information retrieval on World Wide Web. IEEE Expert., 58–68 (1997)

    Google Scholar 

  18. Heer, J., Chi, E.H.: Identification of web user traffic composition using multi-modal clustering and information scent. In: Proceedings of Workshop on Web Mining, SIAM Conference on Data Mining, pp. 51–58 (2001)

    Google Scholar 

  19. Jain, A.K., Murty, M.N., Flyn, P.J.: Data clustering: A review. ACM Computing Surveys 31(3), 264–323 (1999)

    Article  Google Scholar 

  20. Jansen, J., Spink, A., Bateman, J., Saracevic, T.: Real life information retrieval: a study of user queries on the web. ACM SIGIR Forum 32(1), 5–17 (1998)

    Article  Google Scholar 

  21. Jensen, R., Shen, Q.: Semantic-preserving dimensionality reduction: rough and fuzzy-rough based approach. IEEE Transactions on Knowledge and Data Engineering 16(12) (2004)

    Google Scholar 

  22. Jensen, R., Shen, Q.: Fuzzy-rough attribute reduction with application to web categorization. Fuzzy Sets and Systems 141(3), 469–485 (2004)

    Article  MathSciNet  MATH  Google Scholar 

  23. Kryszkiewicz, M., Lasek, P.: TI-DBSCAN: Clustering with DBSCAN by Means of the Triangle Inequality. In: Szczuka, M., Kryszkiewicz, M., Ramanna, S., Jensen, R., Hu, Q. (eds.) RSCTC 2010. LNCS, vol. 6086, pp. 60–69. Springer, Heidelberg (2010)

    Chapter  Google Scholar 

  24. Kryszkiewicz, M., Lasek, P.: A neighborhood-based clustering by means of the triangle inequality. In: Fyfe, C., Tino, P., Charles, D., Garcia-Osorio, C., Yin, H. (eds.) IDEAL 2010. LNCS, vol. 6283, pp. 284–291. Springer, Heidelberg (2010)

    Chapter  Google Scholar 

  25. Nguyen, H.S.: On the decision table with maximal number of reducts. Electronic Notes in Theoretical Computer Science 82(4), 198–205 (2003)

    Article  MATH  Google Scholar 

  26. Olston, C., Chi, E.H.: ScentTrails: Integrating browsing and searching on the World Wide Web. ACM Transactions on Computer-Human Interaction 10, 177–197 (2003)

    Article  Google Scholar 

  27. Patra, B.K., Hubballi, N., Biswas, S., Nandi, S.: Distance based fast hierarchical clustering method for large datasets. In: Szczuka, M., Kryszkiewicz, M., Ramanna, S., Jensen, R., Hu, Q. (eds.) RSCTC 2010. LNCS, vol. 6086, pp. 50–59. Springer, Heidelberg (2010)

    Chapter  Google Scholar 

  28. Patra, B.K., Nandi, S.: Fast single-link clustering method based on tolerance rough set model. In: Sakai, H., Chakraborty, M.K., Hassanien, A.E., Ślęzak, D., Zhu, W. (eds.) RSFDGrC 2009. LNCS (LNAI), vol. 5908, pp. 414–422. Springer, Heidelberg (2009)

    Chapter  Google Scholar 

  29. Pawlak, Z., Grzymala-Busse, J., Slowinski, R., Ziarko, W.: Rough sets. Communications of the ACM 38(11), 88–95 (1995)

    Article  Google Scholar 

  30. Pirolli, P., Card, S.K.: Information foragings. Psychological Review 106, 643–675 (1999)

    Article  Google Scholar 

  31. Pirolli, P.: Computational models of information scent-following in a very large browsable text collection. In: Proceedings ACM CHI 1997- Conference on Human Factors in Computing Systems, pp. 3–10 (1997)

    Google Scholar 

  32. Pirolli, P.: The use of proximal information scent to forage for distal content on the World Wide Web. In: Working with Technology in Mind: Brunswikian, Resources for Cognitive Science and Engineering, Oxford University Press, Oxford (2004)

    Google Scholar 

  33. Rocchio, J.J.: Relevance feedback in information retrieval, pp. 313–343. Prentice Hall, Englewood Cliffs (1971)

    Google Scholar 

  34. Salton, G., McGill, M.: An Introduction to Modern Information Retrieval. Mc-Graw-Hill, New York (1983)

    MATH  Google Scholar 

  35. Spath, H.: Cluster Analysis Algorithms for Data Reduction and Classification. Ellis Horwood, Chichester (1980)

    MATH  Google Scholar 

  36. Vechtomova, O., Karamuftuoglu, M.: Elicitation and use of relevance feedback information. Information Processing and Management 42, 191–206 (2006)

    Article  Google Scholar 

  37. Vijaya, P.A., Murty, M.N., Subramanian, D.K.: Leaders-Subleaders: an efficient hierarchical clustering algorithm for large data sets. Pattern Recognition Letters 25(4), 505–513 (2004)

    Article  Google Scholar 

  38. Zhong, N., Dong, J., Ohsuga, S.: Using rough set with heuristics for feature selection. Journal of Intelligent Information System 16, 199–214 (2001)

    Article  MATH  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Computer Science, University of Delhi, Delhi, India

    Punam Bedi & Suruchi Chawla

Authors
  1. Punam Bedi
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Suruchi Chawla
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. University of Manitoba, R3T 5V6, Winnipeg, MB, Canada

    James F. Peters

  2. University of Warsaw, 02-097, Warsaw, Poland

    Andrzej Skowron  & Dominik Slezak  & 

  3. Kyushu Institute of Technology, 804, Tobata, Kitakyushu, Japan

    Hiroshi Sakai

  4. Jadavpur University, Kolkata, Indian Statistical Institute, Kolkata, India

    Mihir Kumar Chakraborty

  5. Cairo University, Orman, Giza, Egypt

    Aboul Ella Hassanien

  6. Zhangzhou Normal University, Fujian, China

    William Zhu

Rights and permissions

Reprints and permissions

Copyright information

© 2011 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Bedi, P., Chawla, S. (2011). High Scent Web Page Recommendations Using Fuzzy Rough Set Attribute Reduction. In: Peters, J.F., et al. Transactions on Rough Sets XIV. Lecture Notes in Computer Science, vol 6600. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-21563-6_2

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-21563-6_2

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-21562-9

  • Online ISBN: 978-3-642-21563-6

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation