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| Content Provider | Semantic Scholar |
|---|---|
| Author | Linden, Greg Smith, Brent Bullet, Jean-Luc |
| Copyright Year | 2001 |
| Abstract | R ecommendation algorithms are best known for their use on e-commerce Web sites,1 where they use input about a customer’s interests to generate a list of recommended items. Many applications use only the items that customers purchase and explicitly rate to represent their interests, but they can also use other attributes, including items viewed, demographic data, subject interests, and favorite artists. At Amazon.com, we use recommendation algorithms to personalize the online store for each customer. The store radically changes based on customer interests, showing programming titles to a software engineer and baby toys to a new mother. The click-through and conversion rates — two important measures of Web-based and email advertising effectiveness — vastly exceed those of untargeted content such as banner advertisements and top-seller lists. E-commerce recommendation algorithms often operate in a challenging environment. For example: |
| File Format | PDF HTM / HTML |
| Alternate Webpage(s) | http://www.indabook.org/get/v8g8FfLCue-OZ6VCl9P7ObAJbQgfQCZu5e1UzeY0aaw,/Amazon-Recommendations-UMD-Department-of.pdf |
| Language | English |
| Access Restriction | Open |
| Content Type | Text |
| Resource Type | Article |
