Tweets Can Tell：Activity Recognition Using Hybrid长短期记忆模型
Tweets Can Tell: Activity Recognition using Hybrid Long Short-Term Memory Model
摘要：
本文提出了技术来检测“离线”活动的人从事时，她是推（如餐饮，购物或娱乐），以创建一个动态的用户配置文件，如更好地定位广告的用途。为此，我们提出了一种用于丰富上下文学习的混合LSTM模型，以及对应用和组合具有不同上下文特征的多个基于LSTM的方法的影响的研究。混合模型的表现优于一组基线和最先进的方法。最后，本文提出了一个正交验证与实际情况下的应用。我们的模型为几个知名账户的粉丝生成了离线活动分析，这很能代表这些账户的预期特征。
英文摘要：
This paper presents techniques to detect the "offline" activity a person is engaged in when she is tweeting (such as dining, shopping or entertainment), 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 is shown to outperform a set of baselines and state-of-the-art methods. Finally, this paper presents an orthogonal validation with a real-case application. Our model generates an offline activity analysis for the followers of several well-known accounts, which is quite representative of the expected characteristics of these accounts.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
