对使用物联网和深度强化学习的社交网络的兴趣点建议
1School of Transportation Information, Jilin Communications Polytechnic, Changchun 130015, China.
Mathematical biosciences and engineering : MBE
|November 3, 2023
概括
本研究介绍了一种使用物联网 (IoT) 和深度强化学习 (DRL) 的社交网络感兴趣点 (POI) 推算法. 综合方法显著提高了推准确性和回忆力,以实现个性化的用户体验.
科学领域:
- 计算机科学 计算机科学
- 人工智能的人工智能
- 数据科学数据科学数据科学
背景情况:
- 传统的社交网络 Point of Interest (POI) 推算法在准确性和回忆方面存在局限性.
- 有效的POI推对于电子商务和社交网络的发展至关重要,推动个性化的用户体验.
研究的目的:
- 提出一个先进的社交网络POI推算法,整合物联网 (IoT) 技术和深度强化学习 (DRL).
- 通过利用多种数据源和用户行为分析,提高POI建议的准确性,回忆力和整体质量.
主要方法:
- 设计了一个整合物联网用于数据收集和DRL用于模型构建的框架.
- 利用物联网来探索用户偏好,分析登记行为,并集成多个数据源.
- 开发了一个DRL模型,使用多源数据计算入住概率,以生成POI建议.
主要成果:
- 在社交网络的POI推中,最高准确率达到了98%和最高回忆率达到了97%.
- 显示了较低的平方根平均误差和较短的推时间,最大的推质量为0.92.
- 拟议的算法在建议有效性方面明显优于传统方法.
结论:
- 拟议的物联网和基于DRL的POI推算法与现有方法相比,提供了更高的性能.
- 这种方法可以有效地应用于电子商务,提供个性化的产品和服务建议,促进社会经济的增长.
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