Online recommendation in non-stationary environments based on knowledge graph enhancement and time-varying reward mechanism

doi:10.1007/s10489-025-07083-z

CSpace

	Online recommendation in non-stationary environments based on knowledge graph enhancement and time-varying reward mechanism
	Li, Jiaxin 1,2; Fang, Jinyun 1,2
	2026-02-01
发表期刊	APPLIED INTELLIGENCE
ISSN	0924-669X
卷号	56 期号:3 页码:22
摘要	Online recommendation systems quickly develop personalized recommendations based on users' historical feedback, thereby improving user experience and increasing platform revenue. Contextual Multi-Armed Bandits (CMAB) model based on reinforcement learning can achieve an effective balance between exploration and utilization, thereby maximizing long-term returns. In this work, we propose a novel CMAB model for online recommendation, which introduces two key innovations: (1) Knowledge Graph-driven Thompson Sampling (KG-TS) that enriches context by constructing a dynamic knowledge graph from user-item interactions to alleviate data sparsity, and (2) Time-Varying Reward Mechanism (TV-RM) that dynamically updates graph edges based on real-time feedback to adapt to non-stationary environments. The integrated algorithm, named KG-TV-TS, is designed to handle sparse and evolving recommendation scenarios. Experiments on three public datasets demonstrate that KG-TV-TS consistently outperforms state-of-the-art bandit algorithms in both recommendation accuracy and cumulative regret, especially under sparse and non-stationary conditions.
关键词	Online recommendation system Contextual Multi-Armed bandits (CMAB) Time-Varying reward mechanism (TV-RM) Knowledge graph (KG) Thompson sampling (TS)
DOI	10.1007/s10489-025-07083-z
收录类别	SCI
语种	英语
WOS研究方向	Computer Science
WOS类目	Computer Science, Artificial Intelligence
WOS记录号	WOS:001679533100002
出版者	SPRINGER
引用统计
文献类型	期刊论文
条目标识符	http://119.78.100.204/handle/2XEOYT63/42828
专题	中国科学院计算技术研究所
通讯作者	Fang, Jinyun
作者单位	1.Chinese Acad Sci, Inst Comp Technol, 6,Zhongguancun Sci Acad South Rd, Beijing 100190, Peoples R China 2.Univ Chinese Acad Sci, Beijing 100049, Peoples R China
推荐引用方式 GB/T 7714	Li, Jiaxin,Fang, Jinyun. Online recommendation in non-stationary environments based on knowledge graph enhancement and time-varying reward mechanism[J]. APPLIED INTELLIGENCE,2026,56(3):22.
APA	Li, Jiaxin,&Fang, Jinyun.(2026).Online recommendation in non-stationary environments based on knowledge graph enhancement and time-varying reward mechanism.APPLIED INTELLIGENCE,56(3),22.
MLA	Li, Jiaxin,et al."Online recommendation in non-stationary environments based on knowledge graph enhancement and time-varying reward mechanism".APPLIED INTELLIGENCE 56.3(2026):22.