Improved recommendation algorithm based on clustering and association rule
Bing Xu, Jianping Ma · 2012
Recommender systems apply knowledge discovery techniques to the problem of making products recommendations during a live customer interaction and they are achieving widespread success in e-commerce nowadays. But the traditional recommendation algorithm makes the quality of system decreased dramatically. In particular, we present an improved recommendation algorithm based on clustering and association rule to calculate the customer's nearest neighbor, and then provide the most appropriate products to meet his needs. The experimental results show the efficiency of our method.