FPLV:增强推者系统的模糊偏好,矢量相似性和用户社区,用于评级预测
Zhan Su1, Haochuan Yang1, Jun Ai1
1School of Optical-Electrical and Computer Engineering, University of Shanghai for Science and Technology, Shanghai, P.R.China.
PloS one
|August 28, 2023
概括
这项研究通过使用模糊逻辑和网络分析来改进排名列表来增强推者系统. 新方法提高了建议的质量,准确性和多样性,同时保持了可解释性.
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:
- 评级预测对于推者系统中个性化推至关重要.
- 提高推清单的质量和可解释性是一个关键的研究挑战.
研究的目的:
- 提高用户列表中推项目的排名质量.
- 确保推系统中的解释性.
- 整合启发式方法,复杂网络理论和模糊技术.
主要方法:
- 利用模糊的会员函数来测量用户属性在多维项目标签向量上.
- 基于这些特征计算用户相似性,用于预测和推.
- 模拟用户相似性网络以提取社区信息和设计推算法.
主要成果:
- 在共同数据集上提高列表排名质量的有效性.
- 预测错误的显著减少.
- 保持推多样性和准确的用户偏好分类.
结论:
- 拟议的算法有效地提高了推者系统的性能.
- 整合模糊技术和网络理论可以提高预测的准确性和多样性.
- 该方法提供了可解释的建议,解决了重大研究差距.
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