User Behavior
User behavior modeling aims to understand and predict how individuals interact with systems, primarily to improve personalization and engagement. Current research focuses on developing sophisticated models, such as deep learning architectures (including transformers, LSTMs, and attention mechanisms), reinforcement learning frameworks, and causal inference methods, to capture complex, long-term user patterns across diverse data modalities (text, clicks, time series). These advancements have significant implications for various applications, including recommender systems, personalized advertising, and the design of more effective human-computer interfaces, particularly in e-commerce and social media.
Papers
Financial Risk Assessment via Long-term Payment Behavior Sequence Folding
Yiran Qiao, Yateng Tang, Xiang Ao, Qi Yuan, Ziming Liu, Chen Shen, Xuehao Zheng
GOT4Rec: Graph of Thoughts for Sequential Recommendation
Zewen Long, Liang Wang, Shu Wu, Qiang Liu, Liang Wang
LIBER: Lifelong User Behavior Modeling Based on Large Language Models
Chenxu Zhu, Shigang Quan, Bo Chen, Jianghao Lin, Xiaoling Cai, Hong Zhu, Xiangyang Li, Yunjia Xi, Weinan Zhang, Ruiming Tang
Sharingan: Extract User Action Sequence from Desktop Recordings
Yanting Chen, Yi Ren, Xiaoting Qin, Jue Zhang, Kehong Yuan, Lu Han, Qingwei Lin, Dongmei Zhang, Saravan Rajmohan, Qi Zhang
FedSub: Introducing class-aware Subnetworks Fusion to Enhance Personalized Federated Learning in Ubiquitous Systems
Mattia Giovanni Campana, Franca Delmastro