Abstract
This paper presents novel approaches to deal with ambiguous or under-specified user queries in search engines. We propose two algorithms for automatic query suggestion that are based on query logs. Furthermore, we propose a novel approach of diversifying the suggestions in order to improve user experience and present a novel adaptation of the MMR diversification algorithm to this problem. We propose two novel query-similarity measures that are utilised by the algorithm. We also present promising preliminary experimental results that are conducted on real data.
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
Agrawal, R., Gollapudi, S., Halverson, A., Ieong, S.: Diversifying search results. In: WSDM, pp. 5–14 (2009)
Anand, S.S., Mobasher, B.: Intelligent Techniques for Web Personalization. In: Mobasher, B., Anand, S.S. (eds.) ITWP 2003. LNCS (LNAI), vol. 3169, pp. 1–36. Springer, Heidelberg (2005)
Carbonell, J., Goldstein, J.: The use of mmr, diversity-based reranking for reordering documents and producing summaries. In: SIGIR 1998: Proceedings of the 21st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 335–336. ACM, New York (1998)
Clarke, C.L.A., Kolla, M., Cormack, G.V., Vechtomova, O., Ashkan, A., Büttcher, S., MacKinnon, I.: Novelty and diversity in information retrieval evaluation. In: SIGIR, pp. 659–666 (2008)
Paul, C., et al.: Multiple approaches to analysing query diversity. In: Proceedings of the 32nd Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 734–735. ACM (2009)
Goffman, W.: A searching procedure for information retrieval. Information Storage and Retrieval 2(2), 73–78 (1964)
Levenshtein, V.: Binary Codes for Correcting Deletions, Insertions, and Reversals. Doklady Akademii Nauk SSSR 163(4), 845–848 (1965)
Liu, F., Yu, C., Meng, W.: Personalized web search for improving retrieval effectiveness. IEEE Transactions on Knowledge and Data Engineering 16, 28–40 (2004)
Pirrò, G., Seco, N.: Design, implementation and evaluation of a new semantic similarity metric combining features and intrinsic information content. In: Chung, S. (ed.) OTM 2008, Part II. LNCS, vol. 5332, pp. 1271–1288. Springer, Heidelberg (2008)
Piskorski, J., Sydow, M.: String Distance Metrics for Reference Matching and Search Query Correction. In: Abramowicz, W. (ed.) BIS 2007. LNCS, vol. 4439, pp. 353–365. Springer, Heidelberg (2007), doi:10.1007/978-3-540-72035-5-27
Piskorski, J., Sydow, M., Wieloch, K.: Comparison of string distance metrics for lemmatisation of named entities in polish. pp. 413–427 (2009)
Piskorski, J., Wieloch, K., Sydow, M.: On knowledge-poor methods for person name matching and lemmatization for highly inflectional languages. Information Retrieval 12(3), 275–299 (2009)
Radlinski, F., Dumais, S.: Improving personalized web search using result diversification. In: Proc. of the 29th Annual International ACM SIGIR Conf. on Research and Development in Information Retrieval, pp. 691–692. ACM, NY (2006)
Santos, R.L.T., Macdonald, C., Ounis, I.: Exploiting query reformulations for web search result diversification. In: Proceedings of the 19th International Conference on World Wide Web, WWW 2010, pp. 881–890. ACM, New York (2010)
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 2012 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Sydow, M., Ciesielski, K., Wajda, J. (2012). Introducing Diversity to Log-Based Query Suggestions to Deal with Underspecified User Queries. In: Bouvry, P., Kłopotek, M.A., Leprévost, F., Marciniak, M., Mykowiecka, A., Rybiński, H. (eds) Security and Intelligent Information Systems. SIIS 2011. Lecture Notes in Computer Science, vol 7053. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-25261-7_20
Download citation
DOI: https://doi.org/10.1007/978-3-642-25261-7_20
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-25260-0
Online ISBN: 978-3-642-25261-7
eBook Packages: Computer ScienceComputer Science (R0)