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| Content Provider | ACM Digital Library |
|---|---|
| Author | Martin, Francisco J. |
| Abstract | The number of online services providing users with real-time recommendations has increased exponentially over the last few years. Recommender Systems that were originally only accessible to a limited number of high-tech companies are now widely available through a growing number of both technical choices and vendors. The acceptance however, of automatically delivered recommendations by users depends on numerous factors that go far beyond the algorithms that constitute the major focus of researchers. This talk will share a number of lessons learned over the last ten years creating and operating recommender systems in a multitude of domains, from digital music to fitness plans through personal finance management, and in a multitude of business settings, from lightweight integrations to highly-coupled integrations within secure bank environments. |
| Starting Page | 1 |
| Ending Page | 2 |
| Page Count | 2 |
| File Format | |
| ISBN | 9781605584355 |
| DOI | 10.1145/1639714.1639715 |
| Language | English |
| Publisher | Association for Computing Machinery (ACM) |
| Publisher Date | 2009-10-23 |
| Publisher Place | New York |
| Access Restriction | Subscribed |
| Subject Keyword | Lessons learned Real world applications Recommender systems |
| Content Type | Text |
| Resource Type | Article |
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