Application of Dimensionality Reduction in Recommender System A Case Study

Badrul Sarwar, George Karypis, Joseph Konstan, and John Riedl
WebKDD-2000 Workshop, 2000
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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 large-scale problems.
Research topics: Collaborative filtering