改进遗传算法在实体商业中精准营销研究与实现

Research and implementation of improved genetic algorithm based precision marketing in entity commerce

由于实体商业市场缺乏像电商平台那样的个性化交互平台,因此无法对客户进行精准营销,使得在商业市场上的竞争力越来越弱。为了解决这一问题,引入商家基因库模型,并记录客户在实体店铺中的历史购物行为、关注的产品类别等,结合最佳邻居、效用函数等提出赋有权重的客户偏好模型。利用改进遗传算法对商家基因库模型与客户偏好模型进行匹配,以实现精准营销。研究以大数据为背景,利用Hadoop集群的Map Reduce编程实现改进遗传算法,用以在n维商家空间中快速、精准地找出最符合客户需求的商家。实验结果表明,改进遗传算法相对于传统遗传算法在推荐准确率上平均提升15.6%,在推荐响应时间上提升41.9%。

The entity commerce lacks of personalized interactive platform as the e-commerce,and can′ t perform the precision marketing for client,so the competitiveness becomes weaker in commercial markets. For the above problem,the merchant gene library model is introduced,the historical shopping behavior and concerned product category of entity stores are recorded for clients,and the best neighbor and utility function are combined to propose the client preference model with weighting. The improved genetic algorithm is used to match the merchant gene library model with client preference model to realize the precision marketing. On the basis of big data,the Map Reduce programming of Hadoop cluster is adopted to improve the genetic algorithm,which can quickly and accurately find out the merchant mostly meeting the requirements of client in n-dimensional merchant space. The experimental results show that the recommended accuracy of the improved genetic algorithm is 15.6% higher than that of the traditional genetic algorithm,and the recommended response time is improved by 41.9%.