skip to main content
10.1145/1367798.1367812acmotherconferencesArticle/Chapter ViewAbstractPublication PageslocwebConference Proceedingsconference-collections
research-article

Discovering co-located queries in geographic search logs

Published: 22 April 2008 Publication History

Abstract

A geographic search request contains a query consisting of one or more keywords, and a search-location that the user searches for. In this paper, we study the problem of discovering co-located queries, which are geographic search requests for nearby search-locations. One example co-located query pattern is {"shopping mall", "parking"}. This pattern indicates that people often search "shopping mall" and "parking" over locations close to one another. Co-located queries have many applications, such as query suggestion, location recommendation, and local advertisement. We formally define co-located query patterns and propose two approaches to mining the patterns. Our basic approach is based on an existing spatial mining algorithm. To find more specific co-located queries that only appear in specific regions, we propose a lattice based approach. It divides the geographic space into regions and mines patterns in each region. We also define a locality measure to categorize patterns into local and global. Experimental results show that the lattice based approach outperforms the basic approach in the number of patterns, the quality of patterns, and the proportion of local patterns.

References

[1]
R. Agrawal, T. Imieliński, and A. Swami. Mining association rules between sets of items in large databases. ACM SIGMOD Rec., 22(2):207--216, 1993.
[2]
D. Beeferman and A. Berger. Agglomerative clustering of a search engine query log. Proc. of SIGKDD, pages 407--416, 2000.
[3]
N. Cressie. Statistics for Spatial Data. 1991.
[4]
S. Cucerzan and R. White. Query suggestion based on user landing pages. Proc. of SIGIR, 2007.
[5]
B. Davison, D. Deschenes, and D. Lewanda. Finding Relevant Website Queries. Proc. of WWW'2003, 2003.
[6]
F. Facca and P. Lanzi. Mining interesting knowledge from weblogs: a survey. Data and Knowledge Engineering, 53(3):225--241, 2005.
[7]
B. Fonseca, P. Golgher, E. de Moura, B. Possas, and N. Ziviani. Discovering search engine related queries using association rules. Journal of Web Engineering, 2(4):215--227, 2004.
[8]
J. Han, K. Koperski, and N. Stefanovic. Geominer: a system prototype for spatial data mining. SIGMOD Rec., 26(2):553--556, 1997.
[9]
Y. Huang, J. Pei, and H. Xiong. Mining co-location patterns with rare events from spatial data sets. Geoinformatica, 10(3):239--260, 2006.
[10]
Y. Huang, S. Shekhar, and H. Xiong. Discovering colocation patterns from spatial data sets: A general approach. IEEE TKDE, 16(12):1472--1485, 2004.
[11]
X. X. J. F. Y. L. W.-Y. M. Lee Wang, Chuang Wang and Y. Li. Detecting dominant locations from search queries. In Proc. of SIGIR, New York, NY, USA, 2005. ACM.
[12]
Y. Morimoto. Mining frequent neighboring class sets in spatial databases. In Proc. SIGKDD '01, pages 353--358, New York, NY, USA, 2001. ACM Press.
[13]
A. Ntoulas, J. Cho, and C. Olston. What's new on the web?: the evolution of the web from a search engine perspective. Proc. of WWW'2003, pages 1--12, 2004.
[14]
Y. S. J. D. S. Asadi, J. Xu and X. Zhou. Calculation of Target Locations for Web Resources. Proc. of WISE, pages 277--288, 2006.
[15]
M. Sanderson and J. Kohler. Analyzing geographic queries. In Proc. of the ACM SIGIR Workshop on GIR, 2004.
[16]
J. Srivastava, R. Cooley, M. Deshpande, and P. Tan. Web usage mining: discovery and applications of usage patterns from Web data. ACM SIGKDD Explorations Newsletter, 1(2):12--23, 2000.
[17]
X. Wang and C. Zhai. Learn from web search logs to organize search results. Proc. of SIGIR, 2007.
[18]
J. Wen, J. Nie, and H. Zhang. Query clustering using user logs. ACM TOIS, 20(1):59--81, 2002.
[19]
X. Zhang, N. Mamoulis, D. W. Cheung, and Y. Shou. Fast mining of spatial collocations. In Proc. of SIGKDD, pages 384--393, New York, NY, USA, 2004.

Cited By

View all
  • (2011)Recommending interesting activity-related local entitiesProceedings of the 34th international ACM SIGIR conference on Research and development in Information Retrieval10.1145/2009916.2010099(1161-1162)Online publication date: 24-Jul-2011

Index Terms

  1. Discovering co-located queries in geographic search logs

    Recommendations

    Comments

    Information & Contributors

    Information

    Published In

    cover image ACM Other conferences
    LOCWEB '08: Proceedings of the first international workshop on Location and the web
    April 2008
    192 pages
    ISBN:9781605581606
    DOI:10.1145/1367798
    Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

    In-Cooperation

    Publisher

    Association for Computing Machinery

    New York, NY, United States

    Publication History

    Published: 22 April 2008

    Permissions

    Request permissions for this article.

    Check for updates

    Author Tags

    1. co-location pattern
    2. log mining
    3. spatial data mining

    Qualifiers

    • Research-article

    Conference

    WWW '08

    Acceptance Rates

    Overall Acceptance Rate 4 of 5 submissions, 80%

    Contributors

    Other Metrics

    Bibliometrics & Citations

    Bibliometrics

    Article Metrics

    • Downloads (Last 12 months)1
    • Downloads (Last 6 weeks)0
    Reflects downloads up to 17 Feb 2025

    Other Metrics

    Citations

    Cited By

    View all
    • (2011)Recommending interesting activity-related local entitiesProceedings of the 34th international ACM SIGIR conference on Research and development in Information Retrieval10.1145/2009916.2010099(1161-1162)Online publication date: 24-Jul-2011

    View Options

    Login options

    View options

    PDF

    View or Download as a PDF file.

    PDF

    eReader

    View online with eReader.

    eReader

    Figures

    Tables

    Media

    Share

    Share

    Share this Publication link

    Share on social media