基于机器学习的移动采购平台动态定价模型构建

Construction of Dynamic Pricing Model Based on Machine Learning in Mobile Procurement Platform

摘要:

随着移动采购平台应用日益广泛,实时动态定价已成为市场竞争与提升平台效益的关键。文中研究并实现了一种以XGBoost为核心预测模型的动态定价系统。具体地,采用特征工程以及超参数优化机制,对价格和需求进行高精度预测,结合规则引擎以及模型优化,生成可执行的定价策略;采用微服务框架以及容器化部署方式,通过消息队列和缓存机制来保障高并发场景下系统的实时响应;通过反馈闭环机制实现预测与策略自适应优化提升。

Abstract:

With the increasing adoption of mobile procurement platforms, real-time dynamic pricing has become crucial for market competitiveness and enhancing platform efficiency. This paper investigates and implements a dynamic pricing system that uses XGBoost as its core predictive model. Specifically, it employs feature engineering and hyperparameter optimization to make high-precision predictions of price and demand, and combines a rule engine with model optimization to generate actionable pricing strategies. By utilizing a microservices framework and containerized deployment, the system ensures real-time responsiveness in high-concurrency scenarios through message queues and caching mechanisms. Furthermore, a closed-loop feedback mechanism enables adaptive optimization of predictions and strategies.