A Survey on Representation Learning for User Modeling

被引:0
|
作者
Li, Sheng [1 ]
Zhao, Handong [2 ]
机构
[1] Univ Georgia, Dept Comp Sci, Athens, GA 30602 USA
[2] Adobe Res, San Jose, CA USA
来源
PROCEEDINGS OF THE TWENTY-NINTH INTERNATIONAL JOINT CONFERENCE ON ARTIFICIAL INTELLIGENCE | 2020年
关键词
D O I
暂无
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
Artificial intelligent systems are changing every aspect of our daily life. In the past decades, numerous approaches have been developed to characterize user behavior, in order to deliver personalized experience to users in scenarios like online shopping or movie recommendation. This paper presents a comprehensive survey of recent advances in user modeling from the perspective of representation learning. In particular, we formulate user modeling as a process of learning latent representations for users. We discuss both the static and sequential representation learning methods for the purpose of user modeling, and review representative approaches in each category, such as matrix factorization, deep collaborative filtering, and recurrent neural networks. Both shallow and deep learning methods are reviewed and discussed. Finally, we conclude this survey and discuss a number of open research problems that would inspire further research in this field.
引用
收藏
页码:4997 / 5003
页数:7
相关论文
共 50 条
  • [21] Representation learning in discourse parsing: A survey
    Song Wei
    Liu LiZhen
    SCIENCE CHINA-TECHNOLOGICAL SCIENCES, 2020, 63 (10) : 1921 - 1946
  • [22] Representation learning in discourse parsing: A survey
    SONG Wei
    LIU LiZhen
    Science China(Technological Sciences), 2020, 63 (10) : 1921 - 1946
  • [23] Uncertainty indetification, representation and measurement in user modeling: A methodology
    Zenebe, A
    Norcio, AF
    INNOVATIONS THROUGH INFORMATION TECHNOLOGY, VOLS 1 AND 2, 2004, : 1157 - 1159
  • [24] On a Modeling of Online User Behavior Using Function Representation
    Pesout, Pavel
    Matustik, Ondrej
    MATHEMATICAL PROBLEMS IN ENGINEERING, 2012, 2012
  • [25] A Survey on User Behavior Modeling in Recommender Systems
    He, Zhicheng
    Liu, Weiwen
    Guo, Wei
    Qin, Jiarui
    Zhang, Yingxue
    Hu, Yaochen
    Tang, Ruiming
    PROCEEDINGS OF THE THIRTY-SECOND INTERNATIONAL JOINT CONFERENCE ON ARTIFICIAL INTELLIGENCE, IJCAI 2023, 2023, : 6656 - 6664
  • [26] User Response Modeling in Recommender Systems: A Survey
    M. Shirokikh
    I. Shenbin
    A. Alekseev
    A. Volodkevich
    A. Vasilev
    S. Nikolenko
    Journal of Mathematical Sciences, 2024, 285 (2) : 255 - 293
  • [27] A User Modeling System for Adaptive Learning
    Loc Nguyen
    2014 INTERNATIONAL CONFERENCE ON INTERACTIVE COLLABORATIVE LEARNING (ICL), 2014, : 864 - 866
  • [28] User Modeling for Language Learning in Facebook
    Virvou, Maria
    Troussas, Christos
    Caro, Jaime
    Espinosa, Kurt Junshean
    TEXT, SPEECH AND DIALOGUE, TSD 2012, 2012, 7499 : 345 - 352
  • [29] LaboUr - Machine learning for user modeling
    Pohl, W
    DESIGN OF COMPUTING SYSTEMS: SOCIAL AND ERGONOMIC CONSIDERATIONS, 1997, 21 : 27 - 30
  • [30] MODELING EASE OF LEARNING OF USER INTERFACES
    KHALIFA, M
    REVUE CANADIENNE DES SCIENCES DE L ADMINISTRATION-CANADIAN JOURNAL OF ADMINISTRATIVE SCIENCES, 1995, 12 (03): : 250 - 267