The Refined Push Algorithm of Marketing Big Data based on Artificial Intelligence

A revolution is taking place in the marketing field. This paper explores the application of collaborative filtering algorithm (CFA) in the refined push of marketing big data, and studies whether this method can improve the effect and efficiency of personalized marketing. In order to solve the problem of refined push based on marketing big data, this paper first conducts in-depth mining of users' historical behavior data, and then uses tools such as Pandas to conduct multi-dimensional analysis of different types of users based on these preferences. In addition, this paper uses distributed architectures such as HDFS storage system and MapReduce computing framework, and calls artificial intelligence algorithms such as machine learning to complete the refined push of marketing data. The collaborative filtering algorithm performs well in personalized marketing effects, with the highest effect score reaching 95.92 points, and the accuracy of CFA can reach up to 96.19%.