Online Learning with Implicit User Preferences
published: Jan. 24, 2012, recorded: December 2011, views: 320
Report a problem or upload filesIf you have found a problem with this lecture or would like to send us extra material, articles, exercises, etc., please use our ticket system to describe your request and upload the data.
Enter your e-mail into the 'Cc' field, and we will keep you updated with your request's status.
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.
Link this pageWould you like to put a link to this lecture on your homepage?
Go ahead! Copy the HTML snippet !