Recommender Systems for Large-Scale E-Commerce: Scalable Neighborhood Formation Using Clustering

Badrul Sarwar, George Karypis, Joseph Konstan, and John Riedl
5th International Conference on Computer and Information Technology (ICCIT), 2002
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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:pr oducing 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.
Research topics: Collaborative filtering