Analysis of recommendation algorithms for e-commerce

Recommender systems apply statistical and knowledge dis- covery techniques to the problem of making product recom- mendations 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 par- ticular, we apply a collection of algorithms such as tradi- tional data mining, nearest-neighbor collaborative filtering, and dimensionality reduction on two different data sets. The first data set was derived from the web-purchasing transac- tion of a large E-commerce company whereas the second data set was collected from MovieLens movie recommenda- tion site. For the experimental purpose, we divide the rec- ommendation 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.