This paper presents an analysis of recommendation algorithms for e-commerce. The authors investigate several techniques for analyzing large-scale purchase and preference data to produce useful recommendations. They apply traditional data mining, nearest-neighbor collaborative filtering, and dimensionality reduction on two data sets: one from a large e-commerce company and another from the MovieLens movie recommendation site. The recommendation generation process is divided into three sub-processes: representation of input data, neighborhood formation, and recommendation generation. Different techniques are devised for each sub-process and applied to compare recommendation quality and performance. The paper discusses the challenges of collaborative filtering in e-commerce, including scalability and recommendation quality. It presents new algorithms that are particularly suited for sparse data sets, such as those common in e-commerce applications. The authors also compare the performance of several different recommender algorithms, including original collaborative filtering algorithms, algorithms based on dimensionality reduction, and classical data mining algorithms. The paper uses two datasets for experimental validation: one from a large e-commerce company and another from the MovieLens movie recommendation site. The experiments evaluate the effectiveness of different recommendation algorithms, including association rule-based and collaborative filtering approaches. The results show that dimensionality reduction techniques can improve the scalability and quality of collaborative filtering algorithms. The paper also discusses the impact of neighborhood size, the number of dimensions, and the recommendation generation process on the performance of recommendation systems. The results indicate that the optimal number of neighbors and dimensions depends on the dataset. The authors conclude that new technologies are needed to improve the scalability of recommender systems.This paper presents an analysis of recommendation algorithms for e-commerce. The authors investigate several techniques for analyzing large-scale purchase and preference data to produce useful recommendations. They apply traditional data mining, nearest-neighbor collaborative filtering, and dimensionality reduction on two data sets: one from a large e-commerce company and another from the MovieLens movie recommendation site. The recommendation generation process is divided into three sub-processes: representation of input data, neighborhood formation, and recommendation generation. Different techniques are devised for each sub-process and applied to compare recommendation quality and performance. The paper discusses the challenges of collaborative filtering in e-commerce, including scalability and recommendation quality. It presents new algorithms that are particularly suited for sparse data sets, such as those common in e-commerce applications. The authors also compare the performance of several different recommender algorithms, including original collaborative filtering algorithms, algorithms based on dimensionality reduction, and classical data mining algorithms. The paper uses two datasets for experimental validation: one from a large e-commerce company and another from the MovieLens movie recommendation site. The experiments evaluate the effectiveness of different recommendation algorithms, including association rule-based and collaborative filtering approaches. The results show that dimensionality reduction techniques can improve the scalability and quality of collaborative filtering algorithms. The paper also discusses the impact of neighborhood size, the number of dimensions, and the recommendation generation process on the performance of recommendation systems. The results indicate that the optimal number of neighbors and dimensions depends on the dataset. The authors conclude that new technologies are needed to improve the scalability of recommender systems.