Algorithmically matching items to users in a given context is essential for the success and profitability of large scale recommender systems like content optimization, computational advertising, search, shopping, movie recommendation, and many more. 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 clicks and downstream advertising revenue in the short-term to maximize revenue 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. I will also briefly discuss response prediction techniques for serving ads on the RightMedia Ad exchange.
Bio: Deepak Agarwal is a statistician at Yahoo! who is interested in developing statistical and machine learning methods to enhance the performance of large scale recommender systems. Deepak and his collaborators significantly improved article recommendation on several Yahoo! websites, most notably on the Yahoo! front page (a 200+% improvement in click-rates). He also works closely with teams in computational advertising to deploy elaborate statistical models on the RightMedia Ad Exchange, yet another large scale recommender system. He currently serves as associate editor for the Journal of American Statistical Association (JASA) and IEEE Transaction on Knowledge discovery and Data Engineering (TKDE).