一个基于项目受欢迎程度和用户特征的两阶段推优化算法.
1School of Mathematics & Statistic, Changchun University of Technology, Changchun, China.
Heliyon
|October 10, 2024
概括
本研究介绍了一种两阶段的金融产品推算法 (CPCF-TSP),该算法使用用户人口统计学和人气来提高准确性. 它解决了用户选择流行商品的倾向,增强了金融产品的发现.
科学领域:
- 计算机科学 计算机科学
- 数据科学数据科学数据科学
- 金融技术 金融技术
背景情况:
- 当前的金融产品推系统往往以产品为中心.
- 现有的方法与用户冷启动和受欢迎程度偏差作斗争,用户偏爱用户偏爱用户.
- 热的热的热的热的热的热.
- 这些产品.产品.产品.
研究的目的:
- 提出一种新的两阶段推优化算法,CPCF-TSP.
- 通过整合用户功能和受欢迎程度来增强金融产品的推.
- 为了减轻流行的偏差,并解决用户的冷启动问题.
主要方法:
- 开发了一个两阶段的推优化算法 (CPCF-TSP),结合了项目受欢迎程度和用户特征.
- 引入了人气权重因子以使人气正常化并修改皮尔森的相似性.
- 结合了经过修改的Pearson相似性函数与人气正常化和用户特性.
- 在两步程序中集成了一个协作过算法,以提高精度.
主要成果:
- CPCF-TSP有效地利用用户的人口特征.
- 该算法减轻了只推受欢迎的金融产品的偏见.
- 它通过结合用户特性和规范化的受欢迎程度来提高建模性能.
- 在计算推受欢迎程度和相似度权重时,证明了减少不准确性.
- 在混合推模型中成功解决了用户冷启动问题.
结论:
- CPCF-TSP为金融产品推提供了更精确,更平衡的方法.
- 该算法特别适用于大量用户数据和庞大的产品目录的场景.
- 它通过考虑用户特征和减轻受欢迎程度偏差来提高推的准确性.
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