E-commerce Recommendation Algorithm Based on K-Means Clustering

Nowadays, the amount of information generated by major e-commerce platforms every day shows an exponential "explosive" growth trend, behind which there is a huge immeasurable value, and these information must go through a series of transformations to really be used by businesses and users, so that its real value can be realized. This paper mainly studies the e-commerce recommendation algorithm based on k-means clustering. In this paper, the K-means clustering algorithm (KMCA) is integrated with collaborative filtering, and the e-commerce recommendation model is constructed by using this algorithm. F1 value of KMCA is higher than F1 value of other algorithms, and the highest value is 0.67, indicating that the recommendation effect of K-means clustering algorithm is better than the traditional recommendation algorithm and the improved recommendation algorithm.