Analysis of recommendation algorithms for e-commerce
作者： Badrul Sarwar , George Karypis , Joseph Konstan , John Riedl
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摘要: ABSTRACT Re ommender systems apply statisti al and knowledge disovery te hniques to the problem of making produ t re ommendations during a live ustomer intera tion they are hieving widespread su ess in E-Commer e nowadays. In this paper, we investigate several for analyzing large-s ale pur hase preferen data purpose ing useful ustomers. parti ular, olle algorithms h as traditional mining, nearest-neighbor ollaborative ltering, dimensionality redu on two di erent sets. The rst set was derived from web-pur hasing transa large Eommer ompany whereas se ond ted MovieLens movie ommendation site. For experimental purpose, divide generation pro into three sub esses{ representation input data, neighborhood formation, generation. We devise esses their ombinations our sets ompare quality performan e.
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