图 书 馆
基于上下文的强盗新闻推荐算法的无偏离线评价Unbiased Offline Evaluation of Contextual-bandit-based News Article Recommendation Algorithms |
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| 课程网址: | http://videolectures.net/wsdm2011_li_uoe/ |
| 主讲教师: | Lihong Li |
| 开课单位: | 微软公司 |
| 开课时间: | 2011-08-09 |
| 课程语种: | 英语 |
| 中文简介: |  |
| 课程简介: | Contextual bandit algorithms have become popular for online recommendation systems such as Digg, Yahoo! Buzz, and news recommendation in general. Offline evaluation of the effectiveness of new algorithms in these applications is critical for protecting online user experiences but very challenging due to their "partial-label" nature. Common practice is to create a simulator which simulates the online environment for the problem at hand and then run an algorithm against this simulator. However, creating simulator itself is often difficult and modelling bias is usually unavoidably introduced. In this paper, we introduce a replay methodology for contextual bandit algorithm evaluation. Different from simulator-based approaches, our method is completely data-driven and very easy to adapt to different applications. More importantly, our method can provide provably unbiased evaluations. Our empirical results on a large-scale news article recommendation dataset collected from Yahoo! Front Page conform well with our theoretical results. Furthermore, comparisons between our offline replay and online bucket evaluation of several contextual bandit algorithms show accuracy and effectiveness of our offline evaluation method. |
| 关 键 词: | 模拟器; 算法; 计算机科学 |
| 课程来源: | 视频讲座网 |
| 最后编审: | 2020-06-08：吴雨秋(课程编辑志愿者) |
| 阅读次数: | 299 |