Abstract
Information on the web is growing at a rapid pace and to satisfy the information need of the user on the web is a big challenge. Search engines are the major breakthrough in the field of Information Retrieval on the web. Research has been done in literature to use the Information Scent in Query session mining to generate the web page recommendations. Low computational efficiency and classification accuracy are the main problems that are faced due to high dimensionality of keyword vector of query sessions used for web page recommendation. This paper presents the use of Fuzzy Rough Set Attribute Reduction to reduce the high dimensionality of keyword vectors for the improvement in classification accuracy and computational efficiency associated with processing of input queries. Experimental results confirm the improvement in the precision of search results 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 to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Baeza-Yates, R., Ribeiro-Neto, B.: Modern Information Retrieval. Addison Wesley, Reading (1999)
Bedi, P., Chawla, S.: Improving Information Retrieval Precision using Query log mining and Information Scent. Information Technology Journal, Asian Network for Scientific Information 6(4), 584–588 (2007)
Chawla, S., Bedi, P.: Improving Information Retrieval Precision by Finding Related Queries with similar Information need using Information Scent. In: Proc., ICETET 2008 – The 1st International Conference on Emerging Trends in Engineering and Technology, July 16-18, pp. 486–491. IEEE Computer Society Press and Papers also available in IEEE Xplore (2008)
Chawla, S., Bedi, P.: Personalized Web Search using Information Scent. In: Proc. CISSE 2007 - International Joint Conferences on Computer, Information and Systems Sciences, and Engineering, Technically Co-Sponsored by: Institute of Electrical & Electronics Engineers (2007)
Chawla, S., Bedi, P.: Finding Hubs and authorities using Information scent to improve the Information Retrieval precision. In: Proc. ICAI 2008 -The 2008 International Conference on Artificial Intelligence, WORLDCOMP 2008, July 14-17 (2008)
Chi, E.H., Pirolli, P., Chen, K., Pitkow, J.: Using Information Scent to model User Information Needs and Actions on the Web. In: Proc. ACM CHI 2001 Conference on Human Factors in Computing Systems, pp. 490–497 (2001)
Chouchoulas, A., Shen, Q.: Rough Set Aided Keyword Reduction for Text Categorization. Applied Artificial Intelligence 15(9), 843–873 (2001)
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)
Gudivada, V.N., Raghavan, V.V., Grosky, W., KasanaGottu, R.: Information Retrieval on World Wide Web, pp. 58–68. IEEE expert, Los Alamitos (1997)
Heer, J., Chi, E.H.: Identification of Web User Traffic Composition using Multi-Modal clustering and Information Scent. In: Proc. of Workshop on Web Mining, SIAM Conference on Data Mining, pp. 51–58. SIAM, Philadelphia (2001)
Jansen, M., 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)
Jansen, R., Shen, Q.: Semantic –Preserving Dimensionality Reduction:Rough and Fuzzy – Rough-Based Approach. IEEE Transactions on Knowledge and Data Engineering 16(12) (2004)
Jansen, R., Shen, Q.: Fuzzy-Rough Attribute Reduction with Application to Web Categorization. Fuzzy Set and Systems 141(3), 469–485 (2004)
Pawlak, Z., Grzymala-Busse, J., Slowinski, R., Ziarko, W.: Rough Sets. Communications of the ACM 38(11), 88–95 (1995)
Pirolli, P.: Computational models of information scent-following in a very large browsable text collection. In: Proc. ACM CHI 1997 Conference on Human Factors in Computing Systems, pp. 3–10 (1997)
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)
Zhao, Y., Karypis, G.: Comparison of agglomerative and partitional document clustering Algorithms. In: SIAM Workshop on Clustering High-dimensional Data and its Applications (2002)
Zhao, Y., Karypis, Y.: Criterion functions for document clustering. Technical report, University of Minnesota, Minneapolis, MN (2002)
Zhong, N., Dong, J., Ohsuga, S.: Using Rough Set with Heuristics for Feature Selection. Journal of Intelligent Information System 16, 199–214 (2001)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2009 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Bedi, P., Chawla, S. (2009). 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) Rough Sets, Fuzzy Sets, Data Mining and Granular Computing. RSFDGrC 2009. Lecture Notes in Computer Science(), vol 5908. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-10646-0_23
Download citation
DOI: https://doi.org/10.1007/978-3-642-10646-0_23
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-10645-3
Online ISBN: 978-3-642-10646-0
eBook Packages: Computer ScienceComputer Science (R0)