The views expressed here are those of the individual authors and not necessarily those of the JSM sponsors, their officers, or their staff.
Abstract Details
Activity Number:
|
491
|
Type:
|
Invited
|
Date/Time:
|
Wednesday, August 3, 2011 : 10:30 AM to 12:20 PM
|
Sponsor:
|
Section on Statistical Computing
|
Abstract - #300237 |
Title:
|
Bayesian hierarchical models for recommender systems at Yahoo!
|
Author(s):
|
Deepak Agarwal and Liang Zhang*+
|
Companies:
|
Yahoo Research
|
Address:
|
4401 Great America parkway, Santa Clara, CA, 95054,
|
Keywords:
|
computation advertising ;
spike and slab prior ;
Gamma-Poisson ;
multi-level hierarchical model ;
map-reduce ;
ad exchange
|
Abstract:
|
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.
|
The address information is for the authors that have a + after their name.
Authors who are presenting talks have a * after their name.
Back to the full JSM 2011 program
|
2011 JSM Online Program Home
For information, contact jsm@amstat.org or phone (888) 231-3473.
If you have questions about the Continuing Education program, please contact the Education Department.