Paper Info
Title
Identifying the activities supported by locations with community-authored content
Abstract
Community-authored content, such as location specific reviews, offers a wealth of information about virtually every imaginable location today. In this work, we process Yelp's community-authored reviews to identify a set of potential activities that are supported by the location reviewed. Using 14 test locations we show that the majority of the 40 most common results per location (determined by verb-noun pair frequency) are actual activities supported by their respective locations, achieving a mean precision of up to 79.3%. Although the number of reviews authored for a location has a strong influence on precision, we are able to achieve a precision up to 29.5% when processing only the first 50 reviews, increasing to 45.7% and 57.3% for the first 100 and 200 reviews, respectively. In addition, we present two context-aware services that leverage location-based activity information on a city scale that is accessible through a Web service we developed supporting multiple cities in North America.
Year
DOI
Venue
2010
10.1145/1864349.1864354
UbiComp
Keywords
Field
DocType
north america,test location,community-authored content,actual activity,respective location,location specific review,imaginable location,web service,mean precision,leverage location-based activity information,activity,noun,location
World Wide Web,Leverage (finance),Computer science,Web service
Conference
Citations 
PageRank 
References 
15
0.89
20
Authors
2
Name
Order
Citations
PageRank
David Dearman153029.72
Khai N. Truong22002162.82