在在线交易网络中基于用户趋势的评级扩展
Soohwan Jeong1, Jeongseon Kim1, Byung Suk Lee2
1Department of Computer Science and Engineering, Chungnam National University, Daejeon, Republic of Korea.
PloS one
|April 16, 2024
概括
本研究引入了基于用户趋势的评级缩放,以改善在线用户分类. 这种新方法提高了在社交网络中识别好用户与坏用户的准确性.
科学领域:
- 社交网络分析 社交网络分析
- 机器学习 机器学习
- 数据挖掘 数据挖掘
背景情况:
- 由于主观评分标准,社交网络上的用户评分有所不同.
- 准确的用户分类对于在线平台,特别是匿名平台的信任和安全至关重要.
- 现有的评级系统难以持续地区分用户满意度水平.
研究的目的:
- 提出和评估一种基于用户倾向的新型评级扩展方法.
- 提高在线贸易社交网络中"好"和"坏"用户的分类准确度.
- 为了提高用户分类,链接预测和聚类任务的性能.
主要方法:
- 开发了一个基于用户倾向的评级缩放算法.
- 应用加权图嵌入使用原始和缩放等级.
- 在三个真实世界的在线评级网络数据集上评估了业绩.
主要成果:
- 拟议的评级缩放方法显著超过了原来的评级.
- 在分类准确度方面实现了高达17%的改进.
- 在链接预测 (AUC ROC) 中表现出高达2.5%的改善,在聚类 (Dunn指数) 中表现出21%的改善.
结论:
- 基于用户趋势的评级扩展有效地改善了用户分类和网络分析.
- 该方法提高了在线贸易中声誉系统的可靠性.
- 这种方法提供了对用户行为和网络动态的更细致的理解.
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