基于RFMQ模型的个性化推方法优化研究 - 以跨境电子商务中的户外运动产品为例
Qianlan Chen1,2, Chupeng Chen3, Zubai Jiang4
1College of Physical Education and Health, Guangxi Normal University, Guilin, China.
本研究引入了个性化电子商务建议的RFMQ模型,通过根据价值对客户进行细分来提高准确性. 增强型号显著优于传统方法,特别是在户外运动产品方面.
科学领域:
- 电子商务是电子商务.
- 数据科学数据科学数据科学
- 营销 营销 分析 分析
背景情况:
- 跨境电子商务面临诸如信息过载和不准确的建议等挑战.
- 户外运动产品行业需要有效的个性化营销策略.
研究的目的:
- 提出一个个性化的推优化框架,整合客户价值细分和协作过.
- 解决跨境电子商务的信息过载和提高建议准确性.
主要方法:
- 通过将购买量添加到RFM模型来开发RFMQ模型,以便进行全面的用户行为分析.
- 使用指标细分和K-means集群实现了客户价值细分.
- 根据细分的客户群组设计了一个差异化的协作过推机制.
主要成果:
- 拟议的RFMQ模型在TOPN推任务中明显优于传统的协作过.
- 与传统方法 (0.1709) 相比,指标细分实现了最好的F1得分 (0.3093在TOPN=5) 与传统方法 (0.1709) 相比.
- K-means集群也表现出稳定的改善,达到F1得分为0.267.
结论:
- 该RFMQ模型和细分方法提高了客户细分和推性能.
- 为电子商务平台提供操作解决方案,以实施精确的营销并提高竞争力.
- 特别适合为中小企业提供低成本,高效的个性化建议.
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