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Recommender systems apply knowledge discovery techniques to the problem of making personalized product recommendations during a live customer interaction. These systems, especially the k-nearest neighbor collaborative filtering based ones, are achieving widespread success in E-commerce nowadays. 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. We address the performance issues by scaling up the neighborhood formation process through the use of clustering techniques.