Analysis of Recommendation Algorithms for E-Commerce (2000)
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BibTeX
@MISC{Sarwar00analysisof,
author = {Badrul Sarwar and George Karypis and Joseph Konstan and John Rield},
title = {Analysis of Recommendation Algorithms for E-Commerce},
year = {2000}
}
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Abstract
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 ltering, and dimensionality reduction on two dierent data sets. The rst 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 dierenttechniques for dierent sub processes a...
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