Abstract:
Geographical topic models are used in recommendations, user mobility modeling, event detection, and geo-tagged document mining for topical region and geographical topics. Studying effective geographical topic models ignores efficiency.
However, training geographical topic models is expensive—it can take days to train a small-scale model on a collection of documents with millions of word tokens. This paper proposes the first distributed solution for training geographical topic models, PGeoTopic.
Novel technical components increase parallelism, reduce memory requirement, and lower communication cost in the proposed solution. Our distributed system geographical topic model mining approach scales with model and data size.
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