This technical report, authored by Badrul Sarwar, George Karypis, Joseph Konstan, and John Riedl, from the University of Minnesota, focuses on the analysis of recommendation algorithms for e-commerce. The authors investigate various techniques for analyzing large-scale purchase and preference data to produce useful recommendations. They apply a range of algorithms, including traditional data mining, nearest-neighbor collaborative filtering, and dimensionality reduction, on two datasets: one from a large e-commerce company (Fingerhut Corporations) and another from the MovieLens movie recommendation site. The report is structured into several sections, covering related work, recommender systems, experimental evaluation, and conclusions. Key contributions include a systematic experimental evaluation of different recommender systems and the presentation of new algorithms that are particularly suited for sparse datasets common in e-commerce applications. The authors also discuss the challenges of improving the scalability and quality of recommendations, emphasizing the importance of addressing these challenges simultaneously. The experimental results show that dimensionality reduction techniques can significantly improve the scalability of collaborative filtering algorithms while maintaining or enhancing recommendation quality. The report concludes by highlighting the potential of low-dimensional representations for e-commerce recommendation systems and the need for further research to understand why these techniques work well in some applications but less well in others.This technical report, authored by Badrul Sarwar, George Karypis, Joseph Konstan, and John Riedl, from the University of Minnesota, focuses on the analysis of recommendation algorithms for e-commerce. The authors investigate various techniques for analyzing large-scale purchase and preference data to produce useful recommendations. They apply a range of algorithms, including traditional data mining, nearest-neighbor collaborative filtering, and dimensionality reduction, on two datasets: one from a large e-commerce company (Fingerhut Corporations) and another from the MovieLens movie recommendation site. The report is structured into several sections, covering related work, recommender systems, experimental evaluation, and conclusions. Key contributions include a systematic experimental evaluation of different recommender systems and the presentation of new algorithms that are particularly suited for sparse datasets common in e-commerce applications. The authors also discuss the challenges of improving the scalability and quality of recommendations, emphasizing the importance of addressing these challenges simultaneously. The experimental results show that dimensionality reduction techniques can significantly improve the scalability of collaborative filtering algorithms while maintaining or enhancing recommendation quality. The report concludes by highlighting the potential of low-dimensional representations for e-commerce recommendation systems and the need for further research to understand why these techniques work well in some applications but less well in others.