Recommender systems apply statistical and knowledge discovery
techniques to the problem of making product recommendations during a live
customer interaction and they are achieving widespread success in E-Commerce
nowadays. In this paper, we investigate several techniques for analyzing
large-scale purchase and preference data for the purpose of producing useful
recommendations to customers. In particular, we apply a collection of
algorithms such as traditional data mining, nearest-neighbor collaborative
filtering, and dimensionality reduction on two different data sets. The first
data set was derived from the web-purchasing transaction of a large E-commerce company
whereas the second data set was collected from MovieLens movie recommendation
site. For the experimental purpose, we divide the recommendation generation
process into three sub processes-representation of input data, neighborhood
formation, and recommendation generation. We devise different techniques for
different sub processes and apply their combinations on our data sets to
compare for recommendation quality and performance.