We investigate the use of dimensionality reduction to
improve the performance for a new class of data analysis
software called “recommender systems”. Recommender
systems apply knowledge discovery techniques
to the problem of making personalized product recommendations
during a live customer interaction. 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. Singular
Value Decomposition(SVD)-based recommendation algorithms
can quickly produce high quality recommendations,
but has to undergo very expensive matrix factorization
steps. In this paper, we propose and experimentally
validate a technique that has the potential to
incrementally build SVD-based models and promises
to make the recommender systems highly scalable.