Cited By
View all- de Souza da Silva EAhlers D(2017)Poisson Factorization Models for Spatiotemporal RetrievalProceedings of the 11th Workshop on Geographic Information Retrieval10.1145/3155902.3155912(1-2)Online publication date: 30-Nov-2017
Indirect location recommendation
Article No.: 3, Pages 1 - 8
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.
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Index Terms
- Indirect location recommendation
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- ESRI
- Yandex
- Google Inc.
- NVIDIA
- University of North Texas: University of North Texas
- Microsoft: Microsoft
- ORACLE: ORACLE
- Facebook: Facebook
- SIGSPATIAL: ACM Special Interest Group on Spatial Information
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New York, NY, United States
Publication History
Published: 04 November 2014
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- FCT/MEC
- CITI/FCT/UNL
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SIGSPATIAL '14
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- University of North Texas
- Microsoft
- ORACLE
- SIGSPATIAL
SIGSPATIAL '14: 22nd SIGSPATIAL International Conference on Advances in Geographic Information Systems
November 4 - 7, 2014
Texas, Dallas
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GIR '14 Paper Acceptance Rate 11 of 15 submissions, 73%;
Overall Acceptance Rate 46 of 61 submissions, 75%
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View all- de Souza da Silva EAhlers D(2017)Poisson Factorization Models for Spatiotemporal RetrievalProceedings of the 11th Workshop on Geographic Information Retrieval10.1145/3155902.3155912(1-2)Online publication date: 30-Nov-2017
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