Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/162122
Title: PGeotopic: a distributed solution for mining geographical topic models
Authors: Zhao, Kaiqi
Cong, Gao
Li, Xiucheng
Keywords: Engineering::Computer science and engineering
Issue Date: 2020
Source: Zhao, K., Cong, G. & Li, X. (2020). PGeotopic: a distributed solution for mining geographical topic models. IEEE Transactions On Knowledge and Data Engineering, 34(2), 881-893. https://dx.doi.org/10.1109/TKDE.2020.2989142
Project: MOE2016-T2-1-137 
RG114/19 
RG31/17 
Journal: IEEE Transactions on Knowledge and Data Engineering 
Abstract: Geographical topic models have been used to mine geo-tagged documents for topical region and geographical topics, and also have applications in recommendations, user mobility modeling, event detection, etc. Existing studies focus on learning effective geographical topic models while ignoring the efficiency issue. However, it is very expensive to train geographical topic models - it may take days to train a geographical topic model of a small scale on a collection of documents with millions of word tokens. In this paper, we propose the first distributed solution, called {sf PGeoTopic}PGeoTopic, for training geographical topic models. The proposed solution comprises several novel technical components to increase parallelism, reduce memory requirement, and reduce communication cost. Experiments show that our approach for mining geographical topic models is scalable with both model size and data size on distributed systems.
URI: https://hdl.handle.net/10356/162122
ISSN: 1041-4347
DOI: 10.1109/TKDE.2020.2989142
Rights: © 2020 IEEE. All rights reserved.
Fulltext Permission: none
Fulltext Availability: No Fulltext
Appears in Collections:SCSE Journal Articles

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