Recommender Systems for Large-scale E-Commerce: Scalable Neighborhood Formation Using Clustering (2002)
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BibTeX
@MISC{Sarwar02recommendersystems,
author = {Badrul M. Sarwar and George Karypis and Joseph Konstan and John Riedl},
title = {Recommender Systems for Large-scale E-Commerce: Scalable Neighborhood Formation Using Clustering},
year = {2002}
}
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Abstract
Recommender syPx4fl apply knowledge discovery techniques to the problem of making personalized product recommendations during a live customer interaction. These sye ems, especially the k-nearest neighbor collaborative filtering based ones, are achieving widespread success in E-commerce nowaday s. The tremendous growth of customers and products in recenty ears poses some key challenges for recommender sycomme These are:producing high quality recommendations and performing many recommendations per second for millions of customers and products. New recommender syPxfl 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.
