一个基于深度强化学习的社会形象推系统
Somaye Ahmadkhani1, Mohsen Ebrahimi Moghaddam1
1Shahid Beheshti University, Faculty of Computer Science and Engineering, Tehran, Iran.
PloS one
|April 4, 2024
概括
这项研究引入了一种使用深度强化学习 (DRL) 和情感和风格等新特性的动态图像推系统. 该系统通过适应用户偏好,显著改善了个性化的图像建议.
科学领域:
- 计算机科学 计算机科学
- 人工智能的人工智能
- 信息检索 信息检索
背景情况:
- 信息过载是一个日益严重的问题,原因是大量的动态在线内容.
- 推系统有助于通过分析用户行为来预测兴趣来减轻信息过载.
- 现有的社交形象推系统经常使用静态策略,无法适应不断变化的用户偏好.
研究的目的:
- 开发一个动态的社会形象推系统,适应不断变化的用户偏好.
- 通过结合新的特征,如情绪,风格和个性来增强个性化的推.
- 应对国家代表在推系统的强化学习方面的挑战.
主要方法:
- 一个深度强化学习 (DRL) 框架用于动态图像推.
- 包括用户情绪,风格和个性在内的新奇特征被整合起来,以创建一个特征向量.
- 引入了一种新的状态表示方法,以克服强化学习状态定义的局限性.
主要成果:
- 拟议的动态推系统显著提高了个性化图像推的性能.
- 实验结果显示,与相关作品相比,Recall@k和Precision@k (前100张图像) 的增长率大约为7%-10%.
- 纳入新的特征和国家代表性提高了建议准确性.
结论:
- 开发的动态图像推系统有效地解决了静态方法的局限性.
- 具有增强功能的DRL框架提供了更个性化和适应性的推体验.
- 这项研究在个性化社会形象推领域取得了重大进展.
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