基于双通道LSTM的用户年龄识别方法

传统的年龄回归方法不能学习深层次信息,因此利用能充分挖掘上下文关系信息的深度学习方法来识别用户的年龄。具体而言,提出了一种基于LSTM的年龄回归方法,其能够学习长期依赖关系即建立输入值之间的长相关联系。采用了两种不同的特征,即文本特征和社交特征。为了有效地区分这两种特征,充分利用这两种特征之间的信息,进一步提出了基于双通道LSTM的年龄回归方法,具体实现是在神经网络中加入Merge层,将LSTM分别产生的文本特征表示和社交特征表示结合进行集成学习以充分学习文本特征和社交特征间的联系。实验结果表明,基于双通道LSTM的年龄回归方法能够有效地区分文本特征和社交特征,并且较单个LSTM方法能够取得更好的年龄回归性能。

User age regression with dual-channel LSTM

Traditional age regression approach cant learn context relation, so we utilize deep learning approach which could make full use of context relation to predict users age. Specific implementation is that we propose a age regression approach based on LSTM. LSTM can learn long short-term memory, namely building long relevant connection between input values. We utilize two different features, namely textual and social features. In order to distinguish the two features and make full use of them, we propose a new age regression approach based on dual-channel LSTM. Specific implementation is to add a Merge layer into LSTM, combing text features representation and social features representation generated by LSTM, to fully learn knowledge between textual and social features. Experimental results show that our method can effectively distinguish textual and social features and improve the performance of age regression.