Improved Recommendation Algorithm Based on Clustering and Association Rule

摘要

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 ecommerce nowadays. But the traditional recommendation algorithm makes the quality of system decreased dramatically. In particular,we present a 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.