Working Memory Theory Driven Natural Attribute Prediction Model for Social Media User Profiling
LIU Jinhang1, LI Lin2, LONG Sijie2, WANG Conghui2
1. School of Computer Science, Hubei University of Technology, Wuhan 430068; 2. School of Computer Science and Artificial Intelligence, Wuhan University of Technology, Wuhan 430070
Abstract：Constructing user profiling systems using contents generated by social media user can offer personalized services and precise marketing for e-commerce platform. It is a significant research direction in the field of social media analysis. In this paper, the document-level multimodal data formed by users publishing content chronologically is studied, and the challenges brought by that to user profiling are analyzed. Aiming at the natural attribute primarily related to user gender and birth year, how to deal with and analyze the document-level multimodal data posted by social media users efficiently is studied as well. A natural attribute prediction model for social media user profiling is proposed. Inspired by cognitive psychology, an effective data chunking method is designed via working memory theory to alleviate the problems of semantics broken and synthetic discourse in traditional methods. To solve the problem of user content preference, an attention mechanism is employed to balance task contributions between intra-modal and inter-modal data. Experiments show that the proposed model is superior in user gender and birth year prediction.
刘锦行, 李琳, 龙思杰, 王聪慧. 工作记忆理论驱动的社交媒体用户画像自然属性预测模型[J]. 模式识别与人工智能, 2023, 36(10): 877-889.
LIU Jinhang, LI Lin, LONG Sijie, WANG Conghui. Working Memory Theory Driven Natural Attribute Prediction Model for Social Media User Profiling. Pattern Recognition and Artificial Intelligence, 2023, 36(10): 877-889.
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