Indirect Location Recommendation

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

1 Citation (Scopus)


Recommending interesting locations to users is a challenge for social and productive networks. The evidence of the content produced by users must be considered in this task, which may be simplified by the use of the meta-data associated with the content, i.e., the categorization supported by the network – descriptive keywords and geographic coordinates. In this paper we present an extension to a productive network representation model, originally designed to discover indirect keywords. Our extension adds a spatial dimension to the information that represents the user production, enabling indirect location discovery methods through the interpretation of the network as a graph, solely relying on keywords and locations that categorize or describe productive items. The model and indirect location discovery methods presented in this paper avoid content analysis, and are a new step towards a generic approach to the identifi- cation of relevant information, otherwise hidden from the users. The evaluation of the model extension and methods is accomplished by an experiment that performs a classifi- cation analysis over the Twitter network. The results show that we can efficiently recommend locations to users
Original languageEnglish
Title of host publication8th ACM SIGSPATIAL Workshop on Geographic Information Retrieval
Publication statusPublished - Nov 2014
Event8th ACM International Workshop on Geographic Information Retrieval - Dallas, Texas, Dallas, Texas, United States
Duration: 4 Nov 20144 Nov 2014


Conference8th ACM International Workshop on Geographic Information Retrieval
Abbreviated titleGIR 2014
Country/TerritoryUnited States
CityDallas, Texas
Internet address


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