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Abstract Details
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Activity Number:
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491
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Type:
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Invited
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Date/Time:
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Wednesday, August 3, 2011 : 10:30 AM to 12:20 PM
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Sponsor:
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Section on Statistical Computing
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| Abstract - #300237 |
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Title:
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Bayesian hierarchical models for recommender systems at Yahoo!
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Author(s):
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Deepak Agarwal and Liang Zhang*+
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Companies:
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Yahoo Research
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Address:
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4401 Great America parkway, Santa Clara, CA, 95054,
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Keywords:
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computation advertising ;
spike and slab prior ;
Gamma-Poisson ;
multi-level hierarchical model ;
map-reduce ;
ad exchange
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Abstract:
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We consider the problem of algorithmically recommending items to users who visit Yahoo!. We start from introducing the general background of Yahoo! recommender systems and the map-reduce distributed computing framework. We then show several variations of the Bayesian hierarchical models that provide state-of-the-art performance for users and items with large enough data, and also good performance for new users and new items through a flexible regression model prior on their features. Our model fitting algorithms also combine well with the map-reduce framework that allows us to build user/item profiles on extremely large data sets.
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The address information is for the authors that have a + after their name.
Authors who are presenting talks have a * after their name.
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