关键词: 知识库, 网购意向预测, MapReduce, 知识更新, 图检索

Abstract: The massive data about customer’s online shopping behavior contains a large amount of knowledge which can reflect the rules of customer’s online shopping. The knowledge is the important evidence of customer’s online shopping intention prediction and commodity recommendation. A customer purchase intention prediction system is discussed. The system is based on Hadoop and realizes the purchase intention prediction of online customers by using the knowledge obtained from the historical transaction data. On the basis of the knowledge base which is built with knowledge graph, statistical method and FP-Growth algorithm are leveraged to discover the knowledge from the transaction data in the way of incremental, and then makes the knowledge fusion into the knowledge base. The system can use the knowledge stored in the knowledge base to analyze and predict the purchase intention of online shopping customers thereby realize the goal that improves the operational efficiency of the e-commerce platform and the customer online shopping experience.

Key words: knowledge graph, customer’s online shopping intention prediction, MapReduce, knowledge update, graph retrieving