User attribute classification aims to identify users' attributes (e.g., gender, age and profession) by leveraging user generated content. However, conventional approaches to user attribute classification focus on single attribute classification involving only one user attribute, which completely ignores the relationship among various user attributes. In this paper, we confront a novel scenario in user attribute classification where relevant user attributes are jointly learned, attempting to make the relevant attribute classification tasks help each other. Specifically, we propose a joint learning approach, namely Aux-LSTM, which first learns a proper auxiliary representation between the related tasks and then leverages the auxiliary representation to integrate the learning process in both tasks. Empirical studies demonstrate the effectiveness of our proposed approach to joint learning on relevant user attributes.
REF propose a joint learning approach in order to leverage the relationship features among relevant user attributes.
Joint learning on relevant user attributes in micro-blog
IJCAI
Computer Science