Online Learning with Implicit User Preferences

author: Thorsten Joachims, Department of Computer Science, Cornell University
published: Jan. 24, 2012,   recorded: December 2011,   views: 316
Categories

Slides

Related content

Report a problem or upload files

If 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.
Lecture popularity: You need to login to cast your vote.
  Bibliography

Description

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.

See Also:

Download slides icon Download slides: nipsworkshops2011_joachims_learning_01.pdf (1.8┬áMB)


Help icon Streaming Video Help

Link this page

Would you like to put a link to this lecture on your homepage?
Go ahead! Copy the HTML snippet !

Write your own review or comment:

make sure you have javascript enabled or clear this field: