考虑产品特征属性的替代性需求预测方法

随着电商销售业务的高速发展, 对用户需求进行快速准确预测已成为重要的研究方向. 产品间的替代性对需求有一定影响作用, 且此方面的应用研究在不断深入. 为了提升需求预测精度, 基于畅销预测属性值排序, 利用邻近替代率估计方法, 并结合 Adaboost 预测模型, 构建出一种更优的考虑产品特征属性的替代性需求预测方法, 并通过实验证明该方法行之有效.

Alternative demand forecasting considering product feature attribute

The current e-commerce sales business is developing rapidly, and the rapid and accurate prediction of demands has become a necessary research direction. The substitution of products has a significant influence on demand, and applied research in this aspect is increasing. Based on the ranking of best-selling predictive attribute values, proximity replacement rate estimation and the Adaboost prediction model were applied in this study to develop an improved demand forecasting method with higher accuracy, considering product feature attributes. The experimental findings confirm that the proposed method is accurate and reliable.