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具有内隐用户偏好的在线学习

Online Learning with Implicit User Preferences
课程网址: http://videolectures.net/nipsworkshops2011_joachims_learning/  
主讲教师: Thorsten Joachims
开课单位: 康奈尔大学
开课时间: 2012-01-24
课程语种: 英语
中文简介:
从搜索引擎到智能家居的许多系统旨在不断提高他们为用户提供的实用程序。虽然显然是一个机器学习问题,但用户和学习算法之间的界面应该是什么样的,不太清楚。本讲座着重于学习推荐和搜索中出现的问题,探讨如何将用户与系统之间的交互建模为在线学习过程。特别是,该演讲研究了几种引发隐式反馈的技术,通过用户研究评估其可靠性,然后提出了可以利用这种反馈的在线学习模型和方法。一个关键的发现是隐含的用户反馈以偏好的形式出现,我们的在线学习方法为(近似)理性用户提供了有限的遗憾。
课程简介: Many systems, ranging from search engines to smart homes, aim to continually improve the utility they are providing to their users. While clearly a machine learning problem, it is less clear what the interface between user and learning algorithm should look like. Focusing on learning problems that arise in recommendation and search, this talk explores how the interactions between the user and the system can be modeled as an online learning process. In particular, the talk investigates several techniques for eliciting implicit feedback, evaluates their reliability through user studies, and then proposes online learning models and methods that can make use of such feedback. A key finding is that implicit user feedback comes in the form of preferences, and that our online learning methods provide bounded regret for (approximately) rational users.
关 键 词: 机器学习; 隐式反馈; 交互建模
课程来源: 视频讲座网
最后编审: 2019-09-07:lxf
阅读次数: 29