通过深度强化学习来增强知识图表的建议
Jinlian Zhou1,2, Derong Shen3, Ying Guo4
1School of Computer Science and Engineering, Northeastern University, Shenyang, 110819, China. 543518214@qq.com.
Scientific reports
|January 23, 2026
概括
本研究介绍了RKGnet,这是一种新的推系统框架,使用知识图和深度强化学习来克服冷启动问题并提高可解释性. 通过动态适应用户偏好,RKGnet提高了推的准确性和稳定性.
科学领域:
- 人工智能的人工智能
- 计算机科学 计算机科学
- 信息科学 信息科学 信息科学
背景情况:
- 推系统对于工业和学术界的信息过至关重要.
- 现有的深度学习和协作过方法与冷启动问题作斗争,缺乏可解释性.
研究的目的:
- 提出一个新的算法,RKGnet,解决当前推系统的局限性.
- 为了提高建议的准确性,稳定性和可解释性.
主要方法:
- 开发了RKGnet,这是一个基于知识图的推框架,利用深度强化学习.
- 在知识图中动态代用户偏好,以揭示层次性的潜在兴趣.
- 采用强化学习来进行适应性实体选择和战略优化.
主要成果:
- 与现有的推方法相比,RKGnet表现出优越的性能.
- 在准确性,稳定性和可解释性方面取得了显著的优势.
- 实验结果验证了拟议框架的有效性.
结论:
- RKGnet有效地解决了冷启动问题,并提高了系统的解释性.
- 该框架显示了先进推系统的广泛应用前景.
- 知识图集成与深度强化学习为未来的研究提供了一个有希望的方向.
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