ABSTRACT
The performance of web search engines may often deteriorate due to the diversity and noisy information contained within web pages. User click-through data can be used to introduce more accurate description (metadata) for web pages, and to improve the search performance. However, noise and incompleteness, sparseness, and the volatility of web pages and queries are three major challenges for research work on user click-through log mining. In this paper, we propose a novel iterative reinforced algorithm to utilize the user click-through data to improve search performance. The algorithm fully explores the interrelations between queries and web pages, and effectively finds "virtual queries" for web pages and overcomes the challenges discussed above. Experiment results on a large set of MSN click-through log data show a significant improvement on search performance over the naive query log mining algorithm as well as the baseline search engine.
- Bernard J. Jansen, Amanda Spink, Judy Bateman, and Tefko Saracevic. Real life information retrieval: a study of user queries on the Web, ACM SIGIR Forum, v.32 n.1, p.5--17, Spring 1998. Google ScholarDigital Library
- Brian D.D., David, G.D., and David B.L. Finding Relevant Website Queries, in Proceedings of the Twelfth International World Wide Web Conference, 2003.Google Scholar
- Chien-Kang Huang, Lee-Feng Chien, and Yen-Jen Oyang. Relevant term suggestion in interactive web search based on contextual information in query session logs. JASIST 54(7): 638--649,2003. Google ScholarDigital Library
- Cui H., Wen J.R., Nie J.Y., and Ma W.Y., Query Expansion by Mining User Logs, IEEE Transaction on Knowledge and Data Engineering, Vol. 15, No. 4, July/August 2003. Google ScholarDigital Library
- D. Beeferman and A. Berger. Agglomerative clustering of a search engine query log. In Proceedings of the sixth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pages 407--415, 2000. Google ScholarDigital Library
- Funas, G.W., Landauer,T.K., Gomez,L.M. and Dumais, S.T. 1987. The vocabulary problem in human-system communication. Communications of the ACM 20,11, Pages 946--971, Nov.1987. Google ScholarDigital Library
- G. Jeh and J. Widom. SimRank: A measure of structural-context similarity. In Proceedings of the Eighth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Edmonton, Alberta, Canada, July 2002. Google ScholarDigital Library
- G. Salton and C. Buckley. On the use of spreading activation methods in automatic information, in Proceedings of the 11th annual international ACM SIGIR conference on Research and development in information retrieval, p.147--160, Grenoble, France, May 1988. Google ScholarDigital Library
- H. Small. Co-citation in the scientific literature: A new measure of the relationship between two documents. Journal of the American Society for Information Science, 24:265--269, 1973.Google ScholarCross Ref
- J.-R. Wen, J.-Y. Nie, and H.-J. Zhang. Clustering user queries of a search engine. In Proceedings of the Tenth International World Wide Web Conference, Hong Kong, May 2001. Google ScholarDigital Library
- Joachims T. Optimizing Search Engine using Clickthrough Data. In Proceedings of the ACM Conference on Knowledge Discovery and Data Mining, 2002. Google ScholarDigital Library
- M. M. Kessler. Bibliographic coupling between scientific papers. American Documentation, 14:10--25, 1963.Google ScholarCross Ref
- MSN Search Engine, http://www.msn.com.Google Scholar
- Nick C., David H., and Stephen R. Effective Site Finding using Link Anchor Information, ACM SIGIR'01, New Orleans, 2001. Google ScholarDigital Library
- Nicolas J. Belkin, Helping people find what they don't know, Communications of the ACM, v.43 n.8, p.58--61, Aug. 2000. Google ScholarDigital Library
- Porter, M. An algorithm for suffix stripping. Program, Vol. 14(3), pp. 130--137, 1980.Google ScholarCross Ref
- R. Baeza-Yates and B.Ribeiro-Neto. Modern Information Retrieval. Addison-Wesley, 1999. Google ScholarDigital Library
- Robertson, S.E. et al. Okapi at TREC-3. In Overview of the Third Text REtrieval Conference(TREC-3), 109--126, 1995.Google Scholar
- R. R. Larson. Bibliometrics of the World-Wide Web: An exploratory analysis of the intellectual structure of cyberspace. In Proceedings of the Annual Meeting of the American Society for Information Science, Baltimore, Maryland, October 1996.Google Scholar
- S. Brin and L. Page, The Anatomy of a Large-Scale Hypertextual Web Search Engine, in Proceedings of the 7th international World Wide Web Conference. Vol.7, 1998. Google ScholarDigital Library
- S. Chakrabarti et al., Automatic Resource Compilation by Analyzing Hyperlink Structure and Associated Text, in: Proceedings of the 7th International World Wide Web Conference, 1998. Google ScholarDigital Library
- Thijs W., Wessel K., and Djoerd H., Retrieving Web Pages using Content, Links, URLs and Anchors, TREC10, 2002.Google Scholar
- V. V. Raghavan and H. Sever. On the reuse of past optimal queries. In Proceedings of the ACM SIGIR Conference on Research and Development in Information Retrieval, pages 344--350, Seattle, WA, July 1995. Google ScholarDigital Library
Index Terms
- Optimizing web search using web click-through data
Recommendations
Are click-through data adequate for learning web search rankings?
CIKM '08: Proceedings of the 17th ACM conference on Information and knowledge managementLearning-to-rank algorithms, which can automatically adapt ranking functions in web search, require a large volume of training data. A traditional way of generating training examples is to employ human experts to judge the relevance of documents. ...
Explore click models for search ranking
CIKM '10: Proceedings of the 19th ACM international conference on Information and knowledge managementRecent advances in click model have positioned it as an effective approach to estimate document relevance based on user behavior in web search. Yet, few works have been conducted to explore the use of click model to help web search ranking. In this ...
Re-ranking search results using query logs
CIKM '06: Proceedings of the 15th ACM international conference on Information and knowledge managementThis work addresses two common problems in search, frequently occurring with underspecified user queries: the top-ranked results for such queries may not contain documents relevant to the user's search intent, and fresh and relevant pages may not get ...
Comments