Application of dimensionality reduction in recommender systems�a
case study
(2000)Abstract
We investigate the use of dimensionality reduction to improve performance
for a new class of data analysis software called �recommender systems�.
Recommender systems apply knowledge discovery techniques to the problem
of making product recommendations during a live customer interaction.
These systems are achieving widespread success in E-commerce nowadays,
especially with the advent of the Internet. The tremendous growth
of customers and products poses three key challenges for recommender
systems in the E-commerce domain. These are: producing high quality
recommendations, performing many recommendations per second for millions
of customers and products, and achieving high coverage in the face
of data sparsity. One successful recommender system technology is
collaborative filtering, which works by matching customer preferences
to other customers in making recommendations. Collaborative filtering
has been shown to produce high quality recommendations, but the performance
degrades with the number of customers and products. New recommender
system technologies are needed that can quickly produce high quality
recommendations, even for very largescale problems. This paper presents
two different experiments where we have explored one technology called
Singular Value Decomposition (SVD) to reduce the dimensionality of
recommender system databases. Each experiment compares the quality
of a recommender system using SVD with the quality of a recommender
system using collaborative filtering. The first experiment compares
the effectiveness of the two recommender systems at predicting consumer
preferences based on a database of explicit ratings of products.
The second experiment compares the effectiveness of the two recommender
systems at producing Top-N lists based on a real-life customer purchase
database from an E-Commerce site. Our experience suggests that SVD
has the potential to meet many of the challenges of recommender systems,
under certain conditions. 1
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