Abstract
Much attention has been paid to web search personalization and query optimization over the past decade. With the prevalence of smart phones, the mobile search results for the same query may vary in regard to the user’s location. In order to provide more precise results for users, it’s essential to take geographic location into account along with the user’s input query. In this paper, we try to identify queries that have location intentions. For example, query “weather forecast” has a location intention of local city while “The Statue of Liberty” has a location intention of “New York city”. To identify the location intention behind a query, we propose a novel method to extract a set of features and use neural network to classify queries. In the classification of queries without explicit location names, our experiment shows that our approach achieves 82.5% at F1 measure and outperforms baselines by 4.2%.
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References
Welch, M., Cho, J.: Automatically identifying localizable queries. In: Proceedings of the ACM SIGIR, pp. 1185–1186 (2008)
Kamvar, M., Baluja, S.: Deciphering trends in mobile search. Computer 40(8), 58–62 (2007)
Bennett, N., Radlinski, F., White, R.W., Yilmaz, E.: Inferring and using location metadata to personalize web search. In: Proceedings of the SIGIR, pp. 135–144 (2011)
Matthijs, N., Radlinski, F.: Personalizing web search using long term browsing history. In: Proceedings of the WSDM, pp. 25–34 (2011)
Xiang, B., Jiang, D., Pei, J., Sun, X., Chen, E., Li, H.: Context-aware ranking in web search. In: Proceedings of the SIGIR, pp. 451–458 (2010)
Kharitonov, E., Serdyukov, P.: Demographic context in web search re-ranking. In: Proceedings of the CIKM, pp. 2555–2558 (2012)
Teevan, J., Liebling, D.J., Ravichandran Geetha, G.: Understanding and predicting personal navigation. In: Proceedings of the WSDM, pp. 85–94 (2011)
Bhatia, S., Majumdar, D., Mitra, P.: Query suggestions in the absence of query logs. In: Proceedings of the SIGIR, pp. 795–804 (2011)
Santos, R.L.T., Macdonald, C., Ounis, I.: Learning to rank query suggestions for adhoc and diversity search. Information Retrieval, 1–23 (2012)
Ozertem, U., et al.: Learning to suggest: a machine learning framework for ranking query suggestions. In: Proceedings of the SIGIR, pp. 25–34 (2012)
Lee, U., Liu, Z., Cho, J.: Automatic identification of user goals in web search. In: Proceedings of the WWW, pp. 391–400 (2005)
Yi, J., Maghoul, F., Pedersen, J.: Deciphering mobile search patterns: a study of Yahoo! mobile search queries, In: Proceedings of the WWW, pp. 257–266 (2008)
Kamvar, M., Baluja, S.: A large scale study of wireless search behavior: Google mobile search. In: Proceedings of the AIGCHI, pp. 701–109. ACM (2009)
Chuklin, A., Serdyukov, P., de Rijke, M.: Using intent information to model user behavior in diversified search. In: Braslavski, P., Kuznetsov, S.O., Kamps, J., Rüger, S., Agichtein, E., Segalovich, I., Yilmaz, E., Serdyukov, P. (eds.) ECIR 2013. LNCS, vol. 7814, pp. 1–13. Springer, Heidelberg (2013)
Dhar, S., Swain, S., Mishra, B.S.P.: Query intent classification using semi-supervised learning. In: Proceedings of the IJCA, pp. 40–43 (2014)
Vandrevu, S., Zhang, Y., Tseng, B., Sun, G., Li, X.: Identifying regional sensitive queries in web search. In: Proceedings of the WWW, pp. 507–514 (2008)
Gravano, L., Hatzivassiloglou, V., Lichtenstein, R.: Categorizing web queries according to geographical locality, In: Proceedings of the CIKM, pp. 325–323 (2003)
Bouidghaghen, O., et al.: Personalizing mobile web search for location sensitive queries. In: Mobile Data Management, pp. 110–118. IEEE (2011)
Mitchell, T.: Machine Learning, pp. 36–39. McCraw Hill (1996)
Quinlan, J.R.: Induction of decision trees. Machine Learning 1(1), 81–106 (1996)
Church, K.W., Hanks, P.: Word association norms, mutual information and lexicography. In: Proceedings of the ACL, pp. 76–83 (1989)
Wiener, E., Pedersen, J.O., Weigend, A.S.: A neural network approach to topic spotting. In: Proceedings of the DAIR, pp. 317–332 (1995)
Dunning, T.E.: Accurate methods for the statistics of surprise and coincidence. Computational Linguistics 19(1), 61–74 (1993)
De Boer, P.-T., et al.: A tutorial on the cross-entropy method. Annals of Operations Research 134(1), 19–67 (2005)
Zadeh, L.A.: Fuzzy sets. Information and Controls 8(3), 338–353 (1965)
Luca, A.D., Terminal, S.: A definition of non probabilistic entropy in the setting of the fuzzy set theory. Information and Control 20, 301–312 (1972)
Kotsiantis, S.B., et al.: Supervised machine learning: a review of classification techniques, pp. 3–24 (2007)
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Sun, Y., Li, X., Li, L., Liu, Q., Chen, E., Ma, H. (2015). Mining User’s Location Intention from Mobile Search Log. In: Zhang, S., Wirsing, M., Zhang, Z. (eds) Knowledge Science, Engineering and Management. KSEM 2015. Lecture Notes in Computer Science(), vol 9403. Springer, Cham. https://doi.org/10.1007/978-3-319-25159-2_37
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DOI: https://doi.org/10.1007/978-3-319-25159-2_37
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