University of Cambridge > > Computer Laboratory Wednesday Seminars > Probabilistic Graphical Models in Microsoft's Online Services: TrueSkill, AdPredictor, and Matchbox

Probabilistic Graphical Models in Microsoft's Online Services: TrueSkill, AdPredictor, and Matchbox

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Abstract: Probabilistic Graphical Models play a crucial role in Microsoft’s online services. In this talk, I will describe three powerful applications of graphical model inference in practice. 1. TrueSkill is Xbox Live’s Ranking and Matchmaking system and ensures that gamers online have balanced and exciting matches with equally skilled opponents. 2. AdPredictor is the system that estimates click-through rates (CTR) for ad selection and pricing within Microsoft’s search engine Bing. 3. Matchbox is a large scale Bayesian recommender system that combines aspects of collaborative filtering and content-based recommendation. It is currently being used for tweet recommendation within All three systems have in common that they are based on factor graph models and approximate Bayesian inference. They operate at a very large scale involving millions of gamers, billions of ad impressions, and millions of tweets, respectively. I will discuss the underlying graphical models and inference algorithms as well as application-specific insights and findings. Time permitting, I will show the three systems in action. This is based on joint work with Ralf Herbrich, David Stern, Thomas Borchert, Tom Minka, and Joaquin Quiñonero Candela.

This talk is part of the Computer Laboratory Wednesday Seminars series.

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