图 书 馆
探索开发高效广告系统Exploitation and Exploration in a Performance based Contextual Advertising System |
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| 课程网址: | http://videolectures.net/kdd2010_zhang_eepb/ |
| 主讲教师: | Ruofei Zhang |
| 开课单位: | 雅虎公司 |
| 开课时间: | 2010-10-01 |
| 课程语种: | 英语 |
| 中文简介: |  在线广告的动态市场要求对排名系统进行优化，以始终如一地推广和利用效果更好的广告。在线数据的流式传输特性不可避免地使广告系统在短期内（根据其当前知识）最大化其预期收入（开发）与尝试了解更多有关未知数的知识以提高其知识（探索）之间进行选择，因为后者可能会增加其知识量。未来的收入。在强化学习社区中，对开发与探索（EE）的折衷进行了广泛的研究，但是，直到最近，在线广告才引起人们的广泛关注。在本文中，我们为在线广告开发了两种新颖的EE策略。具体来说，我们的方法可以通过自动学习最佳权衡并结合历史表现的置信度来自适应地平衡EE的两个方面。在经过精心设计的离线模拟框架内，我们将算法应用于基于行业领先性能的上下文广告系统，并使用真实的在线事件日志数据进行了广泛的评估。实验结果和详细的分析揭示了在线广告中EE行为的一些重要发现，并证明了我们的算法在广告覆盖率和点击率（CTR）方面表现优异。 |
| 课程简介: | The dynamic marketplace in online advertising calls for ranking systems that are optimized to consistently promote and capitalize better performing ads. The streaming nature of online data inevitably makes an advertising system choose between maximizing its expected revenue according to its current knowledge in short term (exploitation) and trying to learn more about the unknown to improve its knowledge (exploration), since the latter might increase its revenue in the future. The exploitation and exploration (EE) tradeoff has been extensively studied in the reinforcement learning community, however, not been paid much attention in online advertising until recently. In this paper, we develop two novel EE strategies for online advertising. Specifically, our methods can adaptively balance the two aspects of EE by automatically learning the optimal tradeoff and incorporating confidence metrics of historical performance. Within a deliberately designed offline simulation framework we apply our algorithms to an industry leading performance based contextual advertising system and conduct extensive evaluations with real online event log data. The experimental results and detailed analysis reveal several important findings of EE behaviors in online advertising and demonstrate that our algorithms perform superiorly in terms of ad reach and click-through-rate (CTR). |
| 关 键 词: | 广告系统; 排名优化; 覆盖率; 点击率 |
| 课程来源: | 视频讲座网 |
| 数据采集: | 2020-03-25：zhouxj |
| 最后编审: | 2020-06-11：chenxin |
| 阅读次数: | 74 |