Abstract
Query recommendation is a popular add-on feature of search engines, which provides related and helpful reformulations of a keyword query. Due to the dropping prices of smartphones and the increasing coverage and bandwidth of mobile networks, a large percentage of search engine queries are issued from mobile devices. This makes it possible to provide better query recommendations by considering the physical locations of the query issuers. However, limited research has been done on location-aware query recommendation for search engines. In this paper, we propose an effective spatial proximity measure between a query issuer and a query with a location distribution obtained from its clicked URLs in the query history. Based on this, we extend two popular query recommendation approaches to our location-aware setting, which provides recommendations that are semantically relevant to the original query and their results are spatially close to the query issuer. In addition, we extend the bookmark coloring algorithm for graph proximity search to support our proposed approaches online, with a spatial partitioning based approximation that accelerates the computation of our proposed spatial proximity. We conduct experiments using a real query log, which show that our query recommendation approaches significantly outperform previous work in terms of quality, and they can be efficiently applied online.
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
Notes
- 1.
- 2.
We do not further refine to get an exact result by looking into the locations within the cells, because we believe that those locations near the range r from the user are still spatially relevant (see the location in cell \(c_6\) of Fig. 3).
References
Baeza-Yates, R.A., Hurtado, C.A., Mendoza, M.: Query recommendation using query logs in search engines. In: EDBT Workshops on Current Trends in Database Technology (2004)
Baeza-Yates, R.A., Tiberi, A.: Extracting semantic relations from query logs. In: KDD (2007)
Bar-Yossef, Z., Kraus, N.: Context-sensitive query auto-completion. In: WWW (2011)
Berkhin, P.: Bookmark-coloring algorithm for personalized pagerank computing. Internet Math. 3, 41–62 (2006)
Boldi, P., Bonchi, F., Castillo, C., Donato, D., Gionis, A., Vigna, S.: The query-flow graph: model and applications. In: CIKM, pp. 609–618. ACM (2008)
Bonchi, F., Perego, R., Silvestri, F., Vahabi, H., Venturini, R.: Efficient query recommendations in the long tail via center-piece subgraphs. In: SIGIR, pp. 345–354. ACM (2012)
Cai, F., Liang, S., de Rijke, M.: Time-sensitive personalized query auto-completion. In: CIKM (2014)
Cao, H., Jiang, D., Pei, J., He, Q., Liao, Z., Chen, E., Li, H.: Context-aware query suggestion by mining click-through and session data. In: KDD, pp. 875–883 (2008)
Chen, Y.-Y., Suel, T., Markowetz, A.: Efficient query processing in geographic web search engines. In: SIGMOD, pp. 277–288 (2006)
Craswell, N., Szummer, M.: Random walks on the click graph. In: SIGIR, pp. 239–246. ACM (2007)
Downey, D., Dumais, S.T., Horvitz, E.: Heads and tails: studies of web search with common and rare queries. In: SIGIR (2007)
Guo, J., Cheng, X., Xu, G., Shen, H.: A structured approach to query recommendation with social annotation data. In: CIKM, pp. 619–628. ACM (2010)
Haveliwala, T.H.: Topic-sensitive pagerank. In: WWW, pp. 517–526. ACM (2002)
Huang, Z., Cautis, B., Cheng, R., Zheng, Y.: KB-enabled query recommendation for long-tail queries. In: CIKM, pp. 2107–2112 (2016)
Myllymaki, J., Singleton, D., Cutter, A., Lewis, M., Eblen, S.: Location based query suggestion. US Patent 8,301,639, 30 October 2012
Ni, X., Sun, J., Chen, Z.: Mobile query suggestions with time-location awareness. US Patent Ap. 12/955,758, 31 May 2012
Qi, S., Wu, D., Mamoulis, N.: Location aware keyword query suggestion based on document proximity. TKDE 28(1), 82–97 (2016)
Shokouhi, M.: Learning to personalize query auto-completion. In: SIGIR (2013)
Shokouhi, M., Radinsky, K.: Time-sensitive query auto-completion. In: SIGIR (2012)
Wen, J.-R., Nie, J.-Y., Zhang, H.-J.: Clustering user queries of a search engine. In: WWW (2001)
Yan, X., Guo, J., Cheng, X.: Context-aware query recommendation by learning high-order relation in query logs. In: CIKM, pp. 2073–2076. ACM (2011)
Zhang, Z., Nasraoui, O.: Mining search engine query logs for query recommendation. In: WWW, pp. 1039–1040 (2006)
Zhao, Z., Song, R., Xie, X., He, X., Zhuang, Y.: Mobile query recommendation via tensor function learning. In: IJCAI, pp. 4084–4090 (2015)
Acknowledgements
We thank the reviewers for their valuable comments. This work is partially supported by GRF Grant 17205015 from Hong Kong Research Grant Council. It has also received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No. 657347.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2017 Springer International Publishing AG
About this paper
Cite this paper
Huang, Z., Mamoulis, N. (2017). Location-Aware Query Recommendation for Search Engines at Scale. In: Gertz, M., et al. Advances in Spatial and Temporal Databases. SSTD 2017. Lecture Notes in Computer Science(), vol 10411. Springer, Cham. https://doi.org/10.1007/978-3-319-64367-0_11
Download citation
DOI: https://doi.org/10.1007/978-3-319-64367-0_11
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-64366-3
Online ISBN: 978-3-319-64367-0
eBook Packages: Computer ScienceComputer Science (R0)