基于全域市场数据感知的终端客户推荐

终端客户推荐系统是大型制造商终端营销的一种有效工具.如何在互联网+环境下通过采集全域市场数据，设计一个寻找最佳目标客户的推荐方法成为了一项挑战.为解决这一问题，本文提出一种基于全域市场数据感知的终端客户推荐方法（GMF）.即采用全域分析的思想对全国范围内的客户数据进行预处理，建立全方位，多角度的评估指标，得到目标客户价值.然后通过域子空间分解的方法，在域子空间中对数据进行分解分析，得到某一区域内的客户评价标准，将二者分析结果进行有效融合，通过计算耦合对象相似度，并筛选出最相似的TopN个数据作为最佳目标客户结果集.在大型制造商营销活动所生成的数据集上的实验结果表明：本文提出的推荐算法其性能明显优于当前主流的协同过滤算法.

Terminal Customer Recommendation Based on Global Market Data Perception

The end-customer recommendation system is an effective tool for large-scale manufacturer terminal marketing. How to design a search method for finding the best target customer by collecting global market data in the Internet+ environment has become a challenge. To solve this problem, This study proposes a terminal customer recommendation method based on global market data perception (GMF). That is to use the idea of global analysis to preprocess the customer data nationwide, establish a comprehensive, multi-angle evaluation index, and obtain the target customer value. Then, through the method of domain subspace decomposition, the data is decomposed and analyzed in the domain subspace, and the customer evaluation criteria in a certain region are obtained. The analysis results of the two are effectively merged, and the similarity of the coupled objects is calculated, and the most similar TopN data is used as the best target customer result set. The experimental results on the data set generated by the large-scale manufacturer marketing activities show that the proposed algorithm is significantly better than the current mainstream collaborative filtering algorithm.