基于 EM聚类和用户评分的产品营销与推荐策略研究

摘要

The development of network and Internet technology,especially the development of the“Internet +”and big data has brought great opportunities and challenges to the enterprise product marketing in recent years.Compared to the traditional way of simple and crude price war to achieve marketing objectives,some enterprises win in the battle by leveraging on the data and mining.In this paper,we use data fusion technology to mine user behavior information from the Internet big data, and analyze the individual needs of consumers to recommend consumers their possibly favorite products. Then EM clustering algorithm is used to build model-based collaborative filtering recommendation algorithm.We take the initiative to carry out personalized marketing services,and develop appropriate personalized products marketing strategy to improve the quantity and recommendations of the success rate of the product sold.In this paper,we obtain experimental data from the Amazon online bookstore,and verify the proposed method that integrated EMclustering and user rating has better recommendation results.%网络技术的发展，特别是最近几年来“互联网＋”和大数据的发展，给企业产品营销带来了极大的机遇和挑战。相较于传统的通过简单粗暴的价格战来达到营销目的，一些企业通过对数据的充分利用和挖掘而在商战中获胜。利用数据融合技术从互联网大数据中挖掘用户的行为信息，通过分析消费者的个性化需求，利用 EM聚类算法构建基于模型的协同过滤推荐算法，给消费者推荐可能喜欢的产品，开展个性化主动营销服务；制定相应的个性化产品营销策略，从而提高产品销售的数量及产品推荐的成功率。利用从亚马逊网上书城获取的数据进行实验，验证了综合 EM聚类和用户评分方法具有较好的推荐效果。