Intelligent Analysis Method of E-commerce Data Based on Various Machine Learning Algorithms

Abstract

Under the current rapid development of the e-commerce industry, most e-commerce companies are pursuing to enhance the clicks of products and its conversion rate to buy. And there are many machine learning algorithms for the intelligent analysis of e-commerce data, among which, the most widely used is the recurrent neural network (RNN) and collaborative filtering algorithm. Based on the use of multiple machine learning algorithms, this paper compares the differences in the clicks of products and the purchase conversion rates between the RNN algorithm and the collaborative filtering algorithm. The RNN algorithm can make full use of the behavior sequence time dependence and context information and the collaborative filtering algorithm is based on the similarities between user and product. The evaluation results are as follows: the products clicked by the RNN algorithm are between 18,000 and 25,000, which is significantly higher than the products clicked by the collaborative filtering algorithm. In order to improve user purchase decisions and overall sales efficiency, e-commerce operators can select the RNN algorithm to fully understand the user’s interests and needs, and provide accurate personalized product recommendations.