Tweets Can Tell: Activity Recognition using Hybrid Long Short-Term Memory Model

被引：2

作者：

Cui, Renhao ^{[1
]}

Agrawal, Gagan ^{[1
]}

Ramnath, Rajiv ^{[1
]}

机构：

[1] Ohio State Univ, Dept Comp Sci & Engn, Columbus, OH 43210 USA

来源：

PROCEEDINGS OF THE 2019 IEEE/ACM INTERNATIONAL CONFERENCE ON ADVANCES IN SOCIAL NETWORKS ANALYSIS AND MINING (ASONAM 2019) | 2019年

关键词：

D O I：

10.1145/3341161.3342935

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

This paper presents techniques to detect offline activities of a person when she is tweeting in order to create a dynamic profile of the user, for uses such as better targeting of advertisements. To this end, we propose a hybrid LSTM model for rich contextual learning, along with studies on the effects of applying and combining multiple LSTM based methods with different contextual features. The hybrid model outperforms a set of baselines as well as state-of-the-art methods.

引用

页码：164 / 167

页数：4

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