Indirect location recommendation

Abstract

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 identification of relevant information, otherwise hidden from the users. The evaluation of the model extension and methods is accomplished by an experiment that performs a classification analysis over the Twitter network. The results show that we can efficiently recommend locations to users.