E-commerce recommender system based on improved K-means commodity information management model

Abstract

Since the start of the 21st century, there has been a rapid development of internet technology, causing electronic computers and smartphones to become increasingly popular. The e-commerce industry also experiences quick development. However, the recommendation technology of e-commerce progresses slowly, hindering it from keeping up with the changing times. To enhance the efficiency and accuracy of e-commerce recommender systems, this research introduces an e-commerce recommender system that utilizes an enhanced K-means clustering algorithm to manage commodity information. This method combines the K-means algorithm with a genetic algorithm by encoding the genetic algorithm, setting the initial population, defining the fitness function, and configuring other parameters. The results of the test indicated that the K-mean clustering algorithm and fuzzy C-mean algorithm had a recommendation accuracy of 87.9 % and 84.8 % respectively under the test dataset. The highest recommendation accuracy was observed from the improved K-mean clustering algorithm, which was 91.1 %. The convergence rate of the improved K-mean clustering algorithm was faster by 44 % compared to the traditional K-mean clustering algorithm and 73 % quicker than the fuzzy C-mean algorithm. The study's findings demonstrate that the refined K-means clustering algorithm greatly enhances the recommendation proficiency and precision of the e-commerce recommendation system, in comparison to other comparable algorithms. This research can potentially advance the e-commerce industry and stimulate its growth.