Van Canneyt, Steven, Schockaert, Steven ![]() |
Abstract
Place recommender systems are increasingly being used to find places of a given type that are close to a user-specified location. As it is important for these systems to use an up-to-date database with a wide coverage, there is a need for techniques that are capable of expanding place databases in an automated way. On the other hand, social media are a rich source of geographically distributed information. In this paper, we therefore propose an approach to discover new instances of a given place type by exploiting correlations between terms and locations in geotagged social media. For a variety of place types, our approach is able to find places which are not yet included in popular place databases such as Foursquare or Google Places.
Item Type: | Conference or Workshop Item (Paper) |
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Status: | Published |
Schools: | Computer Science & Informatics |
Subjects: | H Social Sciences > HM Sociology Q Science > QA Mathematics > QA75 Electronic computers. Computer science |
Publisher: | IEEE |
ISBN: | 9781467360579 |
Last Modified: | 27 Oct 2022 10:01 |
URI: | https://orca.cardiff.ac.uk/id/eprint/68602 |
Citation Data
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