Abstract
When a user performs a search on a search engine, the query reflects a particular interest of the user. The interest may either span a short session of a few minutes, or a long period of time like months or years. In the latter, the user may perform searching related to a particular interest from time to time, making the queries related to that interest sporadically distributed in the search log. Identification of these topically related queries is very meaningful, since it can help the search engine better understand the user’s interest and hence deliver better results to the user. In this paper, we propose a method to aggregate topically related queries into interests regardless of where the queries appear in the search log. It first identifies sets of continuous topically-related queries called CTQs and then clusters similar CTQs together to form interests. In order to identify the CTQs accurately, we propose the Pattern-Concept-Time-Based (PCTB) method that utilizes query reformulation patterns, concepts behind the queries and the user’s continuous searching behavior to compute the similarity between two queries. To evaluate the effectiveness of our approach, we employ the AOL search log as our test dataset and develop a search middleware on top of Google for extracting concepts related to the queries. Experimental results show that our method can obtain a high precision and recall on identifying CTQs, which in turn improves the performance of interest identification.
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References
Baeza-Yates, R., Hurtado, C.A., Mendoza, M.: Query recommendation using query logs in search engines. In: Lindner, W., Fischer, F., Türker, C., Tzitzikas, Y., Vakali, A.I. (eds.) EDBT 2004. LNCS, vol. 3268, pp. 588–596. Springer, Heidelberg (2004)
Beeferman, D., Berger, A.: Agglomerative clustering of a search engine query log. In: Proc. of the SIGKDD Conference (2000)
Buzikashvili, N., Jansen, B.J.: Limits of the web log analysis artifacts. In: Workshop on Logging Traces of Web Activity: The Mechanics of Data Collection. WWW Conference (2006)
Church, K.W., Gale, W., Hanks, P., Hindle, D.: Using statistics in lexical analysis. Lexical Acquisition: Exploiting On-Line Resources to Build a Lexicon (1991)
Downey, D.: Models of searching and browsing: languages, studies and applications. In: Proc. of the IJCAI Conference (2007)
Gayo-Avello, D.: A survey on session detection methods in query logs and a proposal for future evaluation. Information Sciences, 1822–1843 (2009)
He, D., Göker, A., Harper, D.J.: Combining evidence for automatic web session identification. Information Processing Management 38 (2002)
Huang, J., Efthimiadis, E.N.: Analyzing and evaluating query reformulation strategies in web search logs. In: CIKM (2009)
Lau, T., Horvitz, E.: Patterns of search: analyzing and modeling web query refinement. In: Proc. of the UM Conference (1999)
Leung, K., Lee, D.: Deriving concept-based user profiles from search engine logs. IEEE Transactions on Knowledge and Data Engineering 99(1) (2007)
Leung, K.W.-T., Fung, H.Y., Lee, D.L.: Constructing concept relation network and its application to personalized web search. In: Proceedings of the 14th International Conference on Extending Database Technology, EDBT/ICDT 2011, pp. 413–424 (2011)
Ozmutlu, H.C., Ozmutlu, F.S.: Automatic new topic identification in search engine transaction logs (2006)
Ozmutlu, H.C., Ozmutlu, S.: Cross-validation of neural network applications for automatic new topic identification. American Society for Information Science and Technology (2008)
Ozmutlu, S., Ozmutlu, H.C., Buyuk, B.: Using conditional probabilities for automatic new topic identification. Online Information Review (2007)
Pass, G., Chowdhury, A., Torgeson, C.: A picture of search. In: Proc. of the INFOSCALE Conference (2006)
Silverstein, C., Marais, H., Henzinger, M., Moricz, M.: Analysis of a very large web search engine query log. SIGIR Forum, 6–12 (1999)
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Zhao, P., Leung, K.WT., Lee, D.L. (2013). Continuous Topically Related Queries Grouping and Its Application on Interest Identification. In: Meng, W., Feng, L., Bressan, S., Winiwarter, W., Song, W. (eds) Database Systems for Advanced Applications. DASFAA 2013. Lecture Notes in Computer Science, vol 7825. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-37487-6_19
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DOI: https://doi.org/10.1007/978-3-642-37487-6_19
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