Abstract
Query ambiguity identification is of vital importance for Web search related studies such as personalized search or diversified ranking. Different from existing solutions which usually require a supervised topic classification process, we propose a query ambiguity identification framework which takes user behavior features collected from click-through logs into consideration. Especially, besides the features collected from query level, we focus on how to tell the differences between clear and ambiguous queries via features extracted from multi-query sessions. Inspired by recent progresses in word representation researches, we propose a query representation approach named “query2vec” which constructs representations from the distributions of queries in query log context. Experiment results based on large scale commercial search engine logs show effectiveness of the proposed framework as well as the corresponding representation approach.
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
Clarke, C.L.A., Kolla, M., Vechtomova, O.: An Effectiveness Measure for Ambiguous and Underspecified Queries. In: Azzopardi, L., Kazai, G., Robertson, S., Rüger, S., Shokouhi, M., Song, D., Yilmaz, E. (eds.) ICTIR 2009. LNCS, vol. 5766, pp. 188–199. Springer, Heidelberg (2009)
Mirco, S., Gauch S.: Personalized Search based on User Search Histories. In: The 2005 IEEE/WIC/ACM International Conference on IEEE, Web Intelligence, pp. 622–628 (2005)
Song, R., et al.: Identification of Ambiguous Queries in Web Search. Information Processing & Management 45(2), 216–229 (2009)
Song, R., et al.: Learning Query Ambiguity Models by using Search Logs. Journal of Computer Science and Technology 25(4), 728–738 (2010)
Mikolov, T., et al.: Distributed Representations of Words and Phrases and their Compositionality. Advances in Neural Information Processing Systems (2013)
Mikolov, T., et al.: Efficient Estimation of Word Representations in Vector Space. arXiv preprint arXiv:1301.3781 (2013)
Broder, A.: A Taxonomy of Web Search. SIGIR Forum 36(2) (2002)
Rose, D.E., Levinson, D.: Understanding User Goals in Web Search. In: Proceedings of the 13th International Conference on World Wide Web, pp. 13–19. ACM (2004)
Zhai, C.X., Cohen, W.W., Lafferty, J.: Beyond Independent Relevance: Methods and Evaluation Metrics for Subtopic Retrieval. In: Proceedings of SIGIR 2013, pp. 10–17. ACM (2003)
Chirita, P.A., et al.: Using ODP Metadata to Personalize Search. In: Proceedings of the 28th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 178–185. ACM (2005)
Carolyn Theresa, H., Jansen, B.J.: Understanding the Specificity of Web Search Queries. In: CHI 2013 Extended Abstracts on Human Factors in Computing Systems, pp. 1827–1832. ACM (2013)
Li, Y., Zheng, Z., Dai, H.K.: KDD CUP-2005 report: Facing a Great Challenge. ACM SIGKDD Explorations Newsletter 7(2), 91–99 (2005)
Shen, D., et al.: Building Bridges for Web Query Classification. In: Proceedings of SIGIR 2006, pp. 131–138. ACM (2006)
Beitzel, S.M., et al.: Varying Approaches to Topical Web Query Classification. In: Proceedings of the 30th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 783–784. ACM (2007)
Westerveld, T., Kraaij, W., Hiemstra, D.: Retrieving Web Pages using Content, Links, Urls and Anchors, pp. 663–672 (2002)
Brenes, D.J., Gayo-Avello, D.: Automatic Detection of Navigational Queries according to Behavioural Characteristics, pp. 41–48. LWA (2008)
Agichtein, E., Zheng, Z.: Identifying Best Bet Web Search Results by Mining Past User Behavior. In: Proceedings of the 12th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 902–908. ACM (2006)
Liu, Y., Zhang, M., Ru, L., Ma, S.: Automatic Query Type Identification based on Click through Information. In: Ng, H.T., Leong, M.-K., Kan, M.-Y., Ji, D. (eds.) AIRS 2006. LNCS, vol. 4182, pp. 593–600. Springer, Heidelberg (2006)
Wang, Y., Agichtein, E.: Query Ambiguity Revisited: Clickthrough Measures for Distinguishing Informational and Ambiguous Queries. In: Human Language Technologies: The 2010 Annual Conference of the North American Chapter of the Association for Computational Linguistics. ACL (2010)
Lee, U., Liu, Z., Cho, J.: Automatic Identification of User Goals in Web Search. In: Proceedings of the 14th International Conference on World Wide Web, pp. 391–400. ACM (2005)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 Springer International Publishing Switzerland
About this paper
Cite this paper
Luo, C., Liu, Y., Zhang, M., Ma, S. (2014). Query Ambiguity Identification Based on User Behavior Information. In: Jaafar, A., et al. Information Retrieval Technology. AIRS 2014. Lecture Notes in Computer Science, vol 8870. Springer, Cham. https://doi.org/10.1007/978-3-319-12844-3_4
Download citation
DOI: https://doi.org/10.1007/978-3-319-12844-3_4
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-12843-6
Online ISBN: 978-3-319-12844-3
eBook Packages: Computer ScienceComputer Science (R0)