Storage method for customer preference information in e-commerce platform based on similarity matching algorithm

Abstract

In order to overcome the problems of small IOPs parameters, long response time and inaccuracy of customer access key information similarity in existing e-commerce platform information storage, this paper proposes a new method of customer preference information storage in e-commerce platform. In this method, crawler algorithm is used to collect customer access information, and key customer access information is extracted by setting indicators. LibSVM classifier is used to classify customer access key information, similarity matching algorithm is used to calculate the similarity of customer access key information, and collaborative filtering recommendation algorithm is used to recommend relevant information for customers, so as to realise the storage of customer preference information in e-commerce platform under the background of artificial intelligence. Experiments show that the proposed storage method greatly improves the IOPs parameters, and the minimum storage response time is 21 ms, which fully shows that the proposed method has better storage performance.