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PEN recsys: a personalized news recommender systems framework
| Content Provider | ACM Digital Library |
|---|---|
| Author | Faltings, Boi Garcin, Florent |
| Abstract | We present the Personalized News (PEN) recommender systems framework, currently in use by a newspaper website to evaluate various algorithms for news recommendations. We briefly describe its system architecture and related components. We show how a researcher can easily evaluate different algorithms thanks to a web-based interface. Finally, we discuss important factors to take into account when conducting online evaluation, and report on our experience when deploying recommendations on a live-traffic website. |
| Starting Page | 3 |
| Ending Page | 9 |
| Page Count | 7 |
| File Format | |
| ISBN | 9781450323024 |
| DOI | 10.1145/2516641.2516642 |
| Language | English |
| Publisher | Association for Computing Machinery (ACM) |
| Publisher Date | 2013-10-13 |
| Publisher Place | New York |
| Access Restriction | Subscribed |
| Subject Keyword | News Recommender system Online evaluation |
| Content Type | Text |
| Resource Type | Article |