城市生活中的日常安排建议基于深度强化学习的学习
IEEE transactions on neural networks and learning systems
|January 24, 2024
概括
本研究介绍了一种使用强化学习的深度活动因素平衡模型,以创建最佳的日常时间表. 该方法有效地推个性化的日常活动序列和位置,节省用户的时间和改进服务.
科学领域:
- 人工智能的人工智能
- 计算机科学 计算机科学
- 运营研究 运营研究
背景情况:
- 有效的日程安排包括优化活动位置 (兴趣点/POI) 和顺序.
- 目前的方法可能无法充分平衡影响每日安排建议的多个因素.
- 个性化的日程安排建议对于用户方便和节省时间至关重要.
研究的目的:
- 提出一种基于强化学习的新型深度活动因素平衡模型,用于每日安排建议 (DSR).
- 开发一个考虑用户位置和需要生成合理的日常活动序列的模型.
- 为了提高个性化的日常规划的效率和有效性.
主要方法:
- 一个深度活动因素平衡网络 (DAFB) 旨在整合影响DSR的各种因素.
- 使用政策梯度的强化学习框架用于训练DAFB参数.
- 基于矩阵的特征存储被用来压缩候选POI的特征空间.
主要成果:
- 拟议的模型在实验性比较中证明了适应性和有效性.
- 使用两个真实世界的数据集,对七种基准方法进行了性能评估.
- 该DAFB模型成功地融合了多个因素,以改善时间表建议.
结论:
- 基于强化学习的DSR模型在个性化日常规划方面取得了重大进展.
- 该方法为优化活动序列和位置选择提供了实用解决方案.
- 该方法被验证为适应性和有效性,用于现实世界的应用.
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