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  1. Proceedings of the first international workshop on Location and the web (LOCWEB '08)
  2. Discovering co-located queries in geographic search logs
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Location and the Web: (LocWeb 2008)
The locative web
A differential notion of place for local search
Determining geographic representations for arbitrary concepts at query time
Urban web crawling
LocalSavvy: aggregating local points of view about news issues
Discovering geographical-specific interests from web click data
Analysis of geographic queries in a search engine log
Core geographical concepts: case Finnish geo-ontology
Acquisition of a vernacular gazetteer from web sources
Annotating and visualizing location data in geospatial web applications
Computable social patterns from sparse sensor data
Modeling and visualizing geo-sensitive queries based on user clicks
Discovering co-located queries in geographic search logs
Geographic web usage estimation by monitoring DNS caches

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Discovering co-located queries in geographic search logs

Content Provider ACM Digital Library
Author Xie, Xing Xiao, Xiangye Wang, Longhao Luo, Qiong
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.
Starting Page 77
Ending Page 84
Page Count 8
File Format PDF
ISBN 9781605581606
DOI 10.1145/1367798.1367812
Language English
Publisher Association for Computing Machinery (ACM)
Publisher Date 2008-04-22
Publisher Place New York
Access Restriction Subscribed
Subject Keyword Co-location pattern Spatial data mining Log mining
Content Type Text
Resource Type Article
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