Transcription of Industry Report Amazon.com Recommendations
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Industry Report Amazon.com Recommendations
Industry Report76 JANUARY FEBRUARY 2003 Published by the IEEE Computer Society1089-7801/03/$ 2003 IEEEIEEE INTERNET Item-to-Item Collaborative FilteringRecommendation algorithms are bestknown for their use on e-commerce Websites,1where they use input about a cus-tomer s interests to generate a list of recommend-ed items. Many applications use only the itemsthat customers purchase and explicitly rate to rep-resent their interests, but they can also use otherattributes, including items viewed, demographicdata, subject interests, and favorite artists. At , we use recommendation algo-rithms to personalize the online store for each cus-tomer.
Amazon.com extensively uses recommendation algorithms to personalize its Web site to each cus-tomer’s interests. Because existing recommendation algorithms cannot scale to Amazon.com’s tens of millions of customers and products, we developed our own. Our algorithm, item-to-item collaborative filtering, scales to massive data sets and produces
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