.png){kind=icon}
返回
PGeoTopic: A Distributed Solution for Mining Geographical Topic Models
.png){kind=icon}
PRE
AI
DOI：10.1109/TKDE.2020.2989142.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
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 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.
Keyword:
Geographical topic model
distributed machine learning
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
10.4
论文数:
6.8K
被引数:
3.2W
机构
引用论文
PLDA+: Parallel Latent Dirichlet Allocation with Data Placement and Pipeline ProcessingPLDA: 具有数据放置和管道处理的并行潜在Dirichlet分配
PREAI
分享收藏Who, Where, When, and What: A Nonparametric Bayesian Approach to Context-aware Recommendation and Search for Twitter Users谁，在哪里，什么时候，什么: 一种非参数贝叶斯的上下文感知推荐和搜索Twitter用户的方法
PREAI
分享收藏Cetyltrimethylammonium bromide intercalated graphene/polypyrrole nanowire composites for high performance supercapacitor electrode.png)
RSC Advances
RSC AdvancesIF0
PREAI
分享收藏Expression of a cDNA encoding the glycolipid‐anchored form of rat acetylcholinesteraseFEBS Letters
FEBS LettersIF0
PREAI
分享收藏
查看更多内容