对于基于用户的动态协作过的适当的重量-度量组合的实验解释
Savas Okyay1,2, Sercan Aygun3,4
1Computer Engineering, Eskisehir Osmangazi University, Eskisehir, Turkey.
PeerJ. Computer science
|November 18, 2024
概括
这项研究引入了推系统的多样化相似度测量,通过动态生成用户参数和减轻测试项目偏差来提高准确性. 这项研究确定了最佳的相似权重和性能指标组合,以提高推质量.
科学领域:
- 数据科学数据科学数据科学
- 机器学习 机器学习
- 推系统是一个推系统.
背景情况:
- 推系统对于个性化体验至关重要,但面临主观偏好和性能变化的挑战.
- 现有的方法经常使用静态参数,限制了在动态环境中的适应性.
研究的目的:
- 提出多样化的相似度测量,以提高推的性能.
- 调查动态参数生成和重要性加权对基于用户的协作过的影响.
主要方法:
- 检查了基于用户的协作过,测量项目偏好概率.
- 验证了测试项目偏差现象,并分析了邻居计数.
- 实现了动态,用户智能的参数生成,不包括感兴趣的项目.
- 整合了重要的权重来分析用户-邻居相似之处.
主要成果:
- 在相似性方程中验证了测试项目偏差现象.
- 证明动态参数生成可以提高可靠性和实时兼容性.
- 通过精心调整的社区检查,确定了相似权重和绩效指标的有效组合.
结论:
- 拟议的方法独特地结合了显著性加权和测试项目偏差缓解.
- 具有动态参数的多样化相似度测量提供了更强大,更准确的推系统架构.
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