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AIC Seminar Series

On Learning from Data using (un-)informative Priors

Harald SteckNetflix

Notice:  Hosted by David Israel. WebEx at, number 629 890 179, sound at 1-888-355-1249, number 749045

Date:  Thursday, June 27th 2013 at 4:00pm

Location:  EJ228 (SRI E building)  (Directions)


In Bayesian statistics, the maximum-a-posteriori estimate is obtained from the prior combined with the likelihood. In this talk, I will discuss, in two examples, how one can choose an (un-)informative Bayesian prior as to influence the obtained result in (un-)expected ways.

The first part of this talk is concerned with the Bayesian approach to structure learning of Bayesian networks. The equivalent sample size (ESS) in the Dirichlet prior over the model parameters is often interpreted as the strength of prior belief, relative to the weight of the training data. In this talk, I will show how the chosen ESS value affects the number of edges in the maximum-a-posteriori estimate of the Bayesian network structure in an unexpected way–from the empty graph to the complete graph.

The second part of this talk is concerned with learning recommender systems that can make personalized suggestions, e.g., of movies, based on a user’s past viewing history. The objective of our recommender system is to rank movies according to each user’s preferences. Users’ preferences can be estimated from their feedback data like plays or ratings of movies, among others. While the extreme data sparsity in the collected data is a well-known problem (each user interacts with only a small fraction of all movies), I will focus on the problem that the feedback data are missing not at random (MNAR). The MNAR nature of the feedback data originates from the fact that users can choose which movies to play or to rate. I will present a simple yet effective approach to tackle this problem using an informative prior with a few parameters that can be determined empirically.

   Bio for Harald Steck

Harald Steck is a data scientist at Netflix, where he develops personalization algorithms and recommender systems. He has over ten years of experience in machine learning, in particular in graphical models and more recently in recommender systems. He has conducted research at various industrial and academic organizations, including Bell Labs, ETH Zurich in Switzerland, MIT AI Lab, as well as Technical University of Munich in Germany, where he obtained a PhD degree in Computer Science in 2001.

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