图 书 馆
学习多种强化学习的不同排名Learning Diverse Rankings with Multi-Armed Bandits |
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| 课程网址: | http://videolectures.net/icml08_kleinberg_ldr/ |
| 主讲教师: | Robert Kleinberg |
| 开课单位: | 康奈尔大学 |
| 开课时间: | 2008-08-06 |
| 课程语种: | 英语 |
| 中文简介: |  |
| 课程简介: | Algorithms for learning to rank Web documents usually assume a document's relevance is independent of other documents. This leads to learned ranking functions that produce rankings with redundant results. In contrast, user studies have shown that diversity at high ranks is often preferred. We present two new learning algorithms that directly learn a diverse ranking of documents based on users' clicking behavior. We show that these algorithms minimize abandonment, or alternatively, maximize the probability that a relevant document is found in the top k positions of a ranking. We show that one of our algorithms asymptotically achieves the best possible payoff obtainable in polynomial time even as user's interests change. The other performs better empirically when user interests are static, and is still theoretically near-optimal in that case. |
| 关 键 词: | Web文档; 排名函数; 多项式时间 |
| 课程来源: | 视频讲座网 |
| 最后编审: | 2019-04-19：lxf |
| 阅读次数: | 77 |