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.
• Customer data is volatile: Each interaction pro-vides valuable customer data, and the algorithm must respond immediately to new information. There are three common approaches to solving the recommendation problem: traditional collabora-tive filtering, cluster models, and search-based methods. Here, we compare these methods with
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