Abstract
Web Search engines explore the existing structure of the web and try to find documents that match the user search criteria .The major challenge for the search engines these days is to determine and satisfy the information need of the user searching the web efficiently and effectively. The approach presented in this paper is to personalize the web search according to the information need of the user using Information Scent in Query log mining. Query log is preprocessed to find the query sessions. Information need associated with query sessions is modeled using Information scent and content of clicked documents. The work done in this paper uses clustering techniques to cluster query sessions with similar information need modeled using Information Scent and content of clicked URLs. The information need of the current user session is used to identify the cluster that has the information need similar to the information need of the current session of the user. The selected cluster is used to recommend useful link on top of next requested result page. Recommended URLs will help the user to find the relevant documents which are closed to his needs and direct the search in a fruitful direction. This approach personalizes the search process to the need of user. Performance of the proposed approach is evaluated with an experimental study of query sessions mining of the “Google” search engine web history data and the experimental results shows the improvement of the Information Retrieval precision.
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.
References
E H. Chi, P. Pirolli, K. Chen and J. Pitkow “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.
R. Baeza-Yates and B. Ribeiro-Neto,“Modern Information Retrieval ” Addison Wesley, 1999.
R. Baeza-Yates, C.A. Hurtado and M. Mendoza “Query Recommendation using query logs in Search engines”, In Advances in Web Intelligence, Second International Atlantic Web Intelligence Conference, AWIC 2004, pp 164-175, 2004.
R J. Wen, ,Y J. Nie, J H Zhang, “Query Clustering Using User Logs”.ACM Transactions on Information Systems,vol 20,No 1,pp 59-81, 2002.
J. Heer and E.H. Chi, “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, 2001.
M. Jansen, A. Spink , J. Bateman and T. Saracevic, “Real life Information retrieval: a Study of user queries on the web”. ACM SIGIR Forum, 32(1): pp 5-17, 1998.
P.Pirolli “Computational models of information scent-following in a very large browsable text collection” In Proc. ACM CHI 97 Conference on Human Factors in Computing Systems, pp 3-10, 1997.
P. Pirolli ,“ 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, 2004.
Punam Bedi and Suruchi Chawla, “Improving Information Retrieval Precision using Query log mining and Information Scent”, Information Technology Journal 6(4):584-588 Asian Network for Scientific Information 2007.
V N. Gudivada , V V. Raghavan, W. Grosky, and R. KasanaGottu ,” Information Retrieval on World Wide Web”. IEEE expert Pg 58-68 1997.
Y. Zhao and G. Karypis, “Comparison of agglomerative and partitional document clustering algorithms”. In SIAM Workshop on Clustering High-dimensional Data and its Applications, 2002.
Y, Zhao and Y, Karypis . “Criterion functions for document clustering”. Technical report, University of Minnesota, Minneapolis, MN, 2002.
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2008 Springer Science+Business Media B.V.
About this paper
Cite this paper
Chawla, S., Bedi, P. (2008). Personalized Web Search Using Information Scent. In: Elleithy, K. (eds) Innovations and Advanced Techniques in Systems, Computing Sciences and Software Engineering. Springer, Dordrecht. https://doi.org/10.1007/978-1-4020-8735-6_90
Download citation
DOI: https://doi.org/10.1007/978-1-4020-8735-6_90
Publisher Name: Springer, Dordrecht
Print ISBN: 978-1-4020-8734-9
Online ISBN: 978-1-4020-8735-6
eBook Packages: Computer ScienceComputer Science (R0)