Working Memory Theory Driven Natural Attribute Prediction Model for Social Media User Profiling

被引：0
|
作者：
Liu J. [1 ]
Li L. [2 ]
Long S. [2 ]
Wang C. [2 ]
机构：
[1] School of Computer Science, Hubei University of Technology, Wuhan
[2] School of Computer Science and Artificial Intelligence, Wuhan University of Technology, Wuhan
来源：
Moshi Shibie yu Rengong Zhineng/Pattern Recognition and Artificial Intelligence | 2023年 / 36卷 / 10期
基金：
中国国家自然科学基金;
关键词：
Natural Attribute; Social Media; User Profiling; Working Memory Theory;
D O I：
10.16451/j.cnki.issn1003-6059.202310002
中图分类号：
学科分类号：
摘要：
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. © 2023 Journal of Pattern Recognition and Artificial Intelligence. All rights reserved.
引用
收藏
页码：877 / 889
页数：12
相关论文
共 38 条
  • [1] CHEN Y, CHEN T Y, DONG Q X, Et al., Research on the Model Construction of Multi-view-Data-Driven User Profile for Social Q&A Platform, Documentation, Information and Knowledge, 5, pp. 64-72, (2019)
  • [2] CHEN J, CHENG L, YANG X, Et al., Joint Learning with Both Classification and Regression Models for Age Prediction, Journal of Physics(Conference Series), 1168, 3, (2019)
  • [3] QIU J, LI L, ZHENG Y P., Micro-Blog User Profiling: A Supervised Clustering Based Approach for Age and Gender Classification, Proc of the 6th International Conference on Behavioral, Economic and Socio-Cultural Computing, (2019)
  • [4] LI Z M, GUO B, SUN Y Q, Et al., An Attention-Based User Profiling Model by Leveraging Multi-modal Social Media Contents, Proc of the International Conference on Cyberspace Data and Intelligence, and Cyber-Living, Syndrome, and Health, pp. 272-284, (2019)
  • [5] WANG Z G, NG P, MA X F, Et al., Multi-passage BERT: A Globally Normalized BERT Model for Open-Domain Question Answering, Proc of the Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing, pp. 5878-5882, (2019)
  • [6] DING Z X, XIA R, YU J F., End-to-End Emotion-Cause Pair Extraction Based on Sliding Window Multi-label Learning, Proc of the Conference on Empirical Methods in Natural Language Processing, pp. 3574-3583, (2020)
  • [7] HOFST魧TTER S, ZAMANI H, MITRA B, Et al., Local Self-Attention over Long Text for Efficient Document Retrieval, Proc of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 2021-2024, (2020)
  • [8] WU C H, WU F Z, QI T, Et al., Neural Gender Prediction in Mi-croblogging with Emotion-Aware User Representation, Proc of the 28th ACM International Conference on Information and Knowledge Management, pp. 2401-2404, (2019)
  • [9] KIM S, JIANG J Y, NAKADA M, Et al., Multimodal Post Attentive Profiling for Influencer Marketing, Proc of the Web Conference, pp. 2878-2884, (2020)
  • [10] SUMAN C, NAMAN A, SAHA S, Et al., A Multimodal Author Profiling System for Tweets, IEEE Transactions on Computational Social Systems, 8, 6, pp. 1407-1416, (2021)
1234