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.
best-selling items, the algorithm typically multi-plies the vector components by the inverse fre-quency (the inverse of the number of customers who have purchased or rated the item), making less well-known items much more relevant. 3 For almost all customers, this vector is extremely sparse. The algorithm generates recommendations
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