Abstract
Search Engines today often return a large volume of results with possibly a few relevant results. The notion of relevance is subjective and depends on the user and the context of search. Re-ranking of these results to reflect the most relevant results to the user, using a user profile built from the relevance feedback has proved to provide good results. Our approach assumes implicit feedback gathered from a search engine query logs and learn a user profile. The user profile typically runs into sparsity problems due to the sheer volume of the WWW. Sparsity refers to the missing weights of certain words in the user profile. In this paper, we present an effective re-ranking strategy that compensates for the sparsity in a user’s profile, by applying collaborative filtering algorithms. Our evaluation results show an improvement in precision over approaches that use only a user’s profile.
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
Kelly, D., Teevan, J.: Implicit feedback for inferring user preference: a bibliography. SIGIR Forum 37, 18–28 (2003)
Kelly, D., Belkin, N.J.: Reading time, scrolling and interaction: Exploring implicit sources of user preferences for relevance feedback during interactive information retrieval. In: Proceedings of the 24th Annual International Conference on Research and Development in Information Retrieval (SIGIR 2001), pp. 408–409 (2001)
Kim, J., Oard, D., Romanik, K.: Using implicit feedback for user modeling in internet and intranet searching. Technical report, College of Library and Information Services, University of Maryland at College Park (2000)
Liu, F., Yu, C., Meng, W.: Personalized web search by mapping user queries to categories. In: Proceedings of the Eleventh International Conference on Information and Knowledge Management (CIKM 2002), pp. 558–565. ACM Press, New York (2002)
Balfe, B.S.E., Freyne, J., Briggs, P., Coyle, M., Boydell, O.: Exploiting query repetition & regularity in an adaptive community-based web search engine. User Modeling and User-Adapted Interaction: The Journal of Personalization Research, 383–423 (2004)
Smyth, B., Balfe, B.O., Bradley, K., Briggs, P., Coyle, M., Freyne, J.: A live-user evaluation of collaborative web search. In: Proceedings of the 19th International Joint Conference on Artificial Intelligence (IJCAI 2005), Edinburgh, Scotland, pp. 1419–1424 (2005)
Balfe, E., Smyth, B.: An analysis of query similarity in collaborative web search. In: Losada, D.E., Fernández-Luna, J.M. (eds.) ECIR 2005. LNCS, vol. 3408, pp. 330–344. Springer, Heidelberg (2005)
Fitzpatrick, L., Dent, M.: Automatic feedback using past queries: Social searching? In: Proceedings of the Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 306–313. ACM Press, New York (1997)
Page, L., Brin, S., Motwani, R., Winograd, T.: The pagerank citation ranking: Bringing order to the web. Technical report, Stanford Digital Library Technologies Project (1998)
Haveliwala, T.H.: Topic-sensitive pangerank. In: Proceedings of the 11th International World Wide Web Conference (WWW 2002), pp. 517–526 (2002)
Pretschner, A., Gauch, S.: Ontology based personalized search. In: ICTAI, pp. 391–398 (1999)
Speretta, M., Gauch, S.: Personalized search based on user search histories. In: Web Intelligence, pp. 622–628 (2005)
Shen., X., Tan., B., Zhai., C.: Context-sensitive information retrieval using implicit feedback. In: Proceedings of SIGIR 2005, pp. 43–50 (2005)
Radlinski, F., Joachims, T.: Evaluating the robustness of learning from implicit feedback. In: ICML Workshop on Learning In Web Search (2005)
Radlinski, F., Joachims, T.: Query chains: Learning to rank from implicit feedback. In: Proceedings of the ACM Conference on Knowledge Discovery and Data Mining (KDD). ACM, New York (2005)
Chidlovskii, B., Glance, N., Grasso, A.: Collaborative re-ranking of search results. In: Proceedings of AAAI 2000 Workshop on AI for Web Search (2000)
Sugiyama, K., Hatano, K., Yoshikawa., M.: Adaptive web search based on user profile constructed without any effort from users. In: Proceedings of WWW 2004, pp. 675–684 (2004)
Lin., H., Xue., G.R., Zeng., H.J., Yu, Y.: Using probabilistic latent semantic analysis for personalized web search. In: Zhang, Y., Tanaka, K., Yu, J.X., Wang, S., Li, M. (eds.) APWEB 2005. LNCS, vol. 3399, pp. 707–717. Springer, Heidelberg (2005)
Hust, A.: Query expansion methods for collaborative information retrieval. Inform., Forsch. Entwickl. 19, 224–238 (2005)
Smyth, B., Balfe, E., Briggs, P., Coyle, M., Freyne, J.: Collaborative web search. In: In Proceedings of the 18th International Joint Conference on Artificial Intelligence, IJCAI 2003, pp. 1417–1419. Morgan Kaufmann, San Francisco (2003)
Freyne, J., Smyth, B., Coyle, M., Balfe, E., Briggs, P.: Further experiments on collaborative ranking in community-based web search. Artificial Intelligence Review 21, 229–252 (2004)
Rohini, U., Vamshi, A.: A collaborative filtering based re-ranking strategy for search in digital libraries. In: Proceedings of 8th ICADL 2005, pp. 192–203 (2005)
Raghavan, V.V., Sever, H.: On the reuse of past optimal queries. In: Proceedings of the Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 344–350 (1995)
Ji-Rong Wen, J.Y., Zhang, H.J.: Query clustering using user logs. ACM Transactions on Information Systems (TOIS) 20, 59–81 (2002)
Glance, N.S.: Community search assistant. In: Proceedings of the International Conference on Intelligent User Interfaces, pp. 91–96. ACM Press, New York (2001)
Vapnik, V.N.: The nature of statistical learning theory (1995)
Yang, Y., Liu, X.: A re-examination of text categorization methods. In: Proceedings of the 22nd Annual International ACM Conference on Research and Development in Information Retrieval (SIGIR 1999), pp. 42–49 (1999)
Joachims, T.: Making large-scale SVM learning practical. Advances in Kernel Methods - Support Vector Learning (1999)
Resnick, P., Iacovou, N., Suchak, M., Bergstorm, J.R.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)
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)
Cohen, W.W., Fan, W.: Web-collaborative filtering: recommending music by crawling the web. Computer Networks 33, 685–698 (2000)
Herlocker, J.L., Konstan, J.A., Borchers, A., Riedl, J.: An algorithmic framework for performing collaborative filtering. In: SIGIR, pp. 230–237 (1999)
Bernard, J., Jansen, A.S.: An analysis of web searching by european alltheweb.com users. Information Processing and Management 41, 361–381 (2005)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2006 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Rohini, U., Ambati, V. (2006). Improving Re-ranking of Search Results Using Collaborative Filtering. In: Ng, H.T., Leong, MK., Kan, MY., Ji, D. (eds) Information Retrieval Technology. AIRS 2006. Lecture Notes in Computer Science, vol 4182. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11880592_16
Download citation
DOI: https://doi.org/10.1007/11880592_16
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-45780-0
Online ISBN: 978-3-540-46237-8
eBook Packages: Computer ScienceComputer Science (R0)