计算机科学
依赖关系(UML)
推荐系统
国家(计算机科学)
透视图(图形)
人工智能
深度学习
事件(粒子物理)
代表(政治)
机器学习
特征学习
状态空间
统计
物理
数学
算法
量子力学
政治
政治学
法学
出处
期刊:IEEE transactions on neural networks and learning systems
[Institute of Electrical and Electronics Engineers]
日期:2022-05-30
卷期号:35 (1): 680-692
标识
DOI:10.1109/tnnls.2022.3176409
摘要
User activities in real systems are usually time-sensitive. But, most of the existing sequential models in recommender systems neglect the time-related signals. In this article, we find that users' temporal behaviors tend to be driven by their regularly changing states, which provides a new perspective on learning users' dynamic preference. However, since the individual state is usually latent, the event space is high dimensional, and meanwhile, temporal dependency of states is personalized and complex; it is challenging to represent, model, and learn the time-evolving patterns of user's state. Focusing on these challenges, we propose a deep structured state learning (DSSL) framework, which is able to learn the representation of temporal states and the complex state dependency for time-sensitive recommendation. Extensive experiments demonstrate that the DSSL achieves competitive results on four real-world recommendation datasets. Furthermore, experiments also show some interesting rules for designing the state dependency network.
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