Abstract
Many Web queries contain both textual keywords and location words. When answering such queries, the association between the textual keywords and locations in a Web page should be taken into account. In this paper, we present a new ranking algorithm for location-related Web search, which is called MapRank. Its main idea is to extract the associations between keywords and locations in Web pages and further use them to improve ranking effectiveness. We first determine map each keyword with specific locations and form a set of < keyword, location > pairs. Then, we compute the location-constrained score for each keyword and combine it into the ranking procedure. We conduct comparison experiments on a real dataset and use the metrics including MAP and NDCG to measure the performance of MapRank. The results show that MapRank is superior to previous methods with respect to different symbolic-location-related queries.
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References
Sanderson, M., Kohler, J.: Analyzing geographic queries. In: Proc. of GIR (2004)
Cao, X., Cong, G., Jensen, C.S., et al.: SWORS: A System for the Efficient Retrieval of Relevant Spatial Web Objects. PVLDB 5(12), 1914–1917 (2012)
Cong, G., et al.: Efficient Retrieval of the Top-k Most Relevant Spatial Web Objects. In: Proc. of VLDB (2009)
Lu, J., Lu, Y., Cong, G.: Reverse Spatial and Textual K Nearest Neighbor Search. In: Proc. of SIGMOD, pp. 349–360 (2011)
Zhou, Y., Xie, X., Wang, C., et al.: Hybrid Index Structures for Location-based Web Search. In: Proc. of CIKM, pp. 155–162. ACM, New York (2005)
Martin, B., Silva, M., et al.: Indexing and Ranking in Geo-IR Systems. In: GIR 2005 (2005)
Andrade, L., et al.: Relevance ranking for geographic information retrieval. In: GIR 2006 (2006)
Jones, C.B., Alani, H., Tudhope, D.: Geographical Information Retrieval with Ontologies of Place. In: Montello, D.R. (ed.) COSIT 2001. LNCS, vol. 2205, pp. 322–335. Springer, Heidelberg (2001)
Larson, R.: Ranking approaches for GIR. SIGSPATIAL Special 3(2) (2011)
Li, H., Li, Z., Lee, W.-C., et al.: A Probabilistic Topic-Based Ranking Framework for location-sensitive domain information retrieval. In: Proc. of SIGIR, pp. 331–338 (2009)
Martins, B., Calado, P.: Learning to Rank for Geographic Information Retrieval. In: Proc. of GIR (2010)
Cai, G.: GeoVSM: An Integrated Retrieval Model for Geographical Information. In: Proc. of GIS, pp. 65–79 (2002)
Zhang, Q., Jin, P., Lin, S., Yue, L.: Extracting Focused Locations for Web Pages. In: Wang, L., Jiang, J., Lu, J., Hong, L., Liu, B. (eds.) WAIM 2011 Workshops. LNCS, vol. 7142, pp. 76–89. Springer, Heidelberg (2012)
Jin, P., Li, X., Chen, H., Yue, L.: CT-Rank: A Time-aware Ranking Algorithm for Web Search. Journal of Convergence Information Technology 5(6), 99–111 (2010)
Yu, B., Cai, G.: A Query-Aware Document Ranking Method for Geographic Information Retrieval. In: Proc. of GIR, pp. 49–54. ACM, New York (2007)
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Jin, P., Zhang, X., Zhang, Q., Lin, S., Yue, L. (2013). Ranking Web Pages by Associating Keywords with Locations. In: Wang, J., Xiong, H., Ishikawa, Y., Xu, J., Zhou, J. (eds) Web-Age Information Management. WAIM 2013. Lecture Notes in Computer Science, vol 7923. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-38562-9_62
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DOI: https://doi.org/10.1007/978-3-642-38562-9_62
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