Abstract: The discovery of social media user interest is of great significance in information overload alleviation,personalized recommendation,and positive guidance of information dissemination.Existing research of interest recognition fails to consider the help of topic information and corresponding category labels information for model learning text features at the same time.Therefore,a user interest recognition method incorporating category labels and topic information is proposed.Firstly,semantic features of text and label sequences are extracted separately by using the BERT pre-trained model,BiLSTM model,and multi-head self-attention mechanism.Then,a label attention mechanism is introduced to make the model pay more attention to the words related to the text’s corresponding category label.Secondly,text topic features are obtained by using the LDA topic model and Word2Vec model.Subsequently,a gating mechanism is designed for feature fusion to enable the model to adaptively merge multiple features,thereby realizing text interest classification.Finally,the number of texts published by users in each interest category is counted,and the interest category with the highest count is determined as users’ interest recognition results.To verify the effectiveness of the proposed method,a Weibo users’ interest recognition dataset is constructed.Experimental results show that the model achieves optimal performance in Weibo text classification and user interest recognition tasks.

Key words: Social network, Interest recognition, Topic model, Label attention mechanism, Feature fusion