Online recommendation in non-stationary environments based on knowledge graph enhancement and time-varying reward mechanism
文献类型：期刊论文
| 作者 | Li, Jiaxin1,2; Fang, Jinyun1,2 |
| 刊名 | APPLIED INTELLIGENCE
 |
| 出版日期 | 2026-02-01 |
| 卷号 | 56期号:3页码:22 |
| 关键词 | Online recommendation system Contextual Multi-Armed bandits (CMAB) Time-Varying reward mechanism (TV-RM) Knowledge graph (KG) Thompson sampling (TS) |
| ISSN号 | 0924-669X |
| DOI | 10.1007/s10489-025-07083-z |
| 英文摘要 | 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. |
| WOS研究方向 | Computer Science |
| 语种 | 英语 |
| WOS记录号 | WOS:001679533100002 |
| 出版者 | SPRINGER |
| 源URL | [http://119.78.100.204/handle/2XEOYT63/42827]  |
| 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.Univ Chinese Acad Sci, Beijing 100049, Peoples R China 2.Chinese Acad Sci, Inst Comp Technol, 6,Zhongguancun Sci Acad South Rd, Beijing 100190, 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. |
入库方式： OAI收割
来源：计算技术研究所
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