Product Prediction and Recommendation in Sustainable E-Commerce Using Association Rule Mining and K-Means Clustering

Abstract

The tremendous growth of customers and products in recent years poses some key challenges for recommender systems. These are producing high quality recommendations and performing many recommendations per second for millions of customers and products. New recommender system technologies are needed that can quickly produce high quality recommendations, even for very large-scale problems. The authors address the performance issues by scaling up the neighborhood formation process through the use of clustering techniques. By using association rule learning, it has been observed that customers who purchase the items t-shirt and jeans have an increasing trend to buy shoes, etc. These systems, especially the k-means clustering-based ones, are achieving widespread success in e-commerce nowadays, and the results are encouraging (i.e., the category silver is preferable as purchasing amount is concerned). Enterprises can use the model to predict the stock and customer for their business sustainability.