Abstract
Web search engines help users find useful information on the WWW. However, when the same query is submitted by different users, typical search engines return the same result regardless of who submitted the query. Generally, each user has different information needs for his/her query. Therefore, the search result should be adapted to users with different information needs. In this paper, we first propose several approaches to adapting search results according to each user’s need for relevant information without any user effort. Experimental results show that search systems that adapt to a user’s preferences can be achieved by constructing user profiles based on modified collaborative filtering.
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
Baeza-Yates, R., Ribeiro-Neto, B.: Modern Information Retrieval. ACM Press, New York (1999)
Balabanovic, M., Shoham, Y.: Fab: Content-Based, Collaborative Recommendation. Communications of the ACM 40(3), 66–72 (1997)
Brin, S., Page, L.: The Anatomy of a Large-Scale Hypertextual Web Search Engine. In: Proc. of the 7th International World Wide Web Conference (WWW7), pp. 107–117 (1998)
Hawking, D.: Overview of the TREC-9Web Track. In: NIST Special Publication 500-249: The Ninth Text REtrieval Conference (TREC-9), pp. 87–102 (2001)
Herlocker, J., Konstan, J., Borchers, A., Riedl, J.: An Algorithmic Framework for Performing Collaborative Filtering. In: Proc. of the 22nd Annual International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR 1999), pp. 230–237 (1999)
IBM Almaden Research Center. Clever Searching, http://www.almaden.ibm.com/cs/k53/clever.html
Konstan, J.A., Miller, B.N., Maltz, D., Herlocker, J.L., Gordon, L.R., Riedl, J.: GroupLens: Applying Collaborative Filtering to Usenet News. Communications of the ACM 40(3), 77–87 (1997)
Manber, U., Patel, A., Robison, J.: Experience with Personalization onYahoo! Communications of the ACM 43(8), 35–39 (2000)
Melville, P., Mooney, R.J., Nagarajan, R.: Content-Boosted Collaborative Filtering for Improved Recommendations. In: Proc. of the 18th National Conference on Artificial Intelligence (AAAI 2002), pp. 187–192 (2002)
Resnick, P., Iacovou, N., Suchak, M., Riedl, J., Bergstorm, P.: GroupLens: An Open Architecture for Collaborative Filtering of Netnews. In: Proc. of the ACM 1994 Conference on Computer Supported Cooperative Work (CSCW 1994), pp. 175–186 (1994)
Rocchio, J.: Relevance Feedback in Information Retrieval. In: Salton, G. (ed.) The Smart Retrieval System: Experiments in Automatic Document Processing, pp. 313–323. Prentice- Hall, Englewood Cliffs (1971)
Schafer, J.B., Konstan, J.A., Riedl, J.: Meta-recommendation Systems: User-controlled Integration of Diverse Recommendations. In: Proc. of the 11th International Conference on Information and Knowledge Management (CIKM 2002), pp. 43–51 (2002)
Sugiyama, K., Hatano, K., Yoshikawa, M., Uemura, S.: A Method of Improving Feature Vector for Web Pages Reflecting the Contents of their Out-Linked Pages. In: Hameurlain, A., Cicchetti, R., Traunmüller, R. (eds.) DEXA 2002. LNCS, vol. 2453, pp. 891–901. Springer, Heidelberg (2002)
Sugiyama, K., Hatano, K., Yoshikawa, M., Uemura, S.: Refinement of TF-IDF Schemes for Web Pages Using their Hyperlinked Neighboring Pages. In: Proc. of the 14th ACM Conference on HyperText and Hypermedia (HT 2003), pp. 198–207 (2003)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2004 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Sugiyama, K., Hatano, K., Yoshikawa, M., Uemura, S. (2004). User-Oriented Adaptive Web Information Retrieval Based on Implicit Observations. In: Yu, J.X., Lin, X., Lu, H., Zhang, Y. (eds) Advanced Web Technologies and Applications. APWeb 2004. Lecture Notes in Computer Science, vol 3007. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-24655-8_69
Download citation
DOI: https://doi.org/10.1007/978-3-540-24655-8_69
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-21371-0
Online ISBN: 978-3-540-24655-8
eBook Packages: Springer Book Archive