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. The store radically changes based on cus-tomer interests, showing programming titles to asoftware engineer and baby toys to a new click-through and conversion rates twoimportant measures of Web-based and emailadvertising effectiveness vastly exceed those ofuntargeted content such as banner advertisementsand top-seller lists.
Industry Report Figure 1. The “Your Recommendations”feature on the Amazon.com homepage.Using this feature,customers can sort recommendations and add their own product ratings. Figure 2. Amazon.com shopping cart recommendations. The recom-mendations are based on the items in the customer’s cart: The Pragmatic Programmer and Physics for ...
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