In this talk, I will begin with an overview of statistical challenges that arise in recommender system problems for web applications like content optimization, online advertising. I will then describe some modeling solutions for a content optimization problem that arises in the context of the Yahoo! Front Page. In particular, I will discuss time series models to track item popularity, explore-exploit/sequential design schemes to enhance performance and matrix factorization models to personalize content to users. For some of the methods, I will present experimental results from an actual system at Yahoo!. I will also provide examples of other applications where the techniques are useful and end with discussion of some open problems in the area.
Tuesday, September 15, 2009
Free and open to the public