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Industry Report Amazon.com Recommendations - UMD

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.

collaborative filtering — focus on finding similar items, not similar customers. For each of the user’s purchased and rated items, the algorithm attempts to find similar items. It then aggregates the simi- ... and thus the approach is inefficient in terms of …

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