基于时间细粒度分析模型的用户行为结构的学习
1School of Economics and Management, Changchun University of Science and Technology, Changchun, China.
PeerJ. Computer science
|February 3, 2025
概括
本研究引入了一种新的算法来分析电子商务购买数据,创建消费者消费模式矩阵. 这种方法增强了对消费者行为和商品关系的理解,优于现有的方法.
科学领域:
- 数据科学数据科学数据科学
- 消费者行为分析 消费者行为分析
- 机器学习 机器学习
背景情况:
- 目前的消费者行为研究依赖于昂贵,有限和滞后的市场研究数据.
- 现有的分析消费模式的方法不足以进行详细的消费者概况.
- 需要有效和可扩展的方法来分析消费者购买数据.
研究的目的:
- 开发一种用于分析电子商务购买数据的算法,以构建一个全面的消费模式矩阵.
- 确定短期和长期的消费者消费矩阵.
- 将这些矩阵整合到一个统一的模式中,用于描述消费者随着时间的推移而表现的行为.
主要方法:
- 使用来自电子商务平台的购买数据.
- 提取短期和长期消费矩阵.
- 处理矩阵以考虑时间细粒度和边际替换率用于集成.
主要成果:
- 拟议的算法成功地提取和整合了消费者消费矩阵.
- 该方法有效地描述了消费者在一定的时间内行为的特征.
- 实验结果显示,与各种数据集的最先进基线相比,性能优越.
结论:
- 开发的算法提供了一种更高效和数据丰富的方法来了解消费者模式.
- 这种方法为商品推和市场分析提供了宝贵的见解.
- 该方法在传统的数据收集和分析技术中显示出了显著的改进.
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