Content Recommendation on Yahoo! Sites
                 Deepak Agarwal, Yahoo! Research

Algorithmically matching articles to users in a given context is
essential for the success and profitability of large scale content
recommendation systems. The objective is to maximize some utility
(e.g. total revenue, total engagement) of interest over a long time
horizon. This is a bandit problem since there is positive utility in
displaying items that may have low mean but high variance. A key
challenge in such bandit problems is the curse of dimensionality.
Bandit problems are also difficult to work with for responses that are
observed with considerable delay (e.g. return visits, confirmation of
a buy). One approach is to optimize multiple competing objectives in
the short-term to achieve the best long-term performance. For instance,
in serving content to users on a website, one may want to optimize
some combination of click rate and time spent reading the article in
the short-term to maximize user engagement in the long-run. In this
talk, I will discuss some of the technical challenges by focusing on a
concrete application - content optimization on the Yahoo! front page.