Author
Listed:
- Hongxia Yang
- Robert Ormandi
- Han‐Yun Tsao
- Quan Lu
Abstract
We consider the problem of estimating occurrence rates of rare events for extremely sparse data using pre‐existing hierarchies and selected features to perform inference along multiple dimensions. In particular, we focus on the problem of estimating click rates for {Advertiser, Publisher, and User} tuples where both the Advertisers and the Publishers are organized as hierarchies that capture broad contextual information at different levels of granularities. Typically, the click rates are low, and the coverage of the hierarchies and dimensions is sparse. To overcome these difficulties, we decompose the joint prior of the three‐dimensional click‐through rate using tensor decomposition and propose a multidimensional hierarchical Bayesian framework (abbreviated as MadHab). We set up a specific framework of each dimension to model dimension‐specific characteristics. More specifically, we consider the hierarchical beta process prior for the Advertiser dimension and for the Publisher dimension respectively and a feature‐dependent mixture model for the User dimension. Besides the centralized implementation, we propose two distributed algorithms through MapReduce and Spark for inferences, which make the model highly scalable and suited for large scale data mining applications. We demonstrate that on a real world ads campaign platform, our framework can effectively discriminate extremely rare events in terms of their click propensity. Copyright © 2016 John Wiley & Sons, Ltd.
Suggested Citation
Hongxia Yang & Robert Ormandi & Han‐Yun Tsao & Quan Lu, 2016.
"Estimating rates of rare events through a multidimensional dynamic hierarchical Bayesian framework,"
Applied Stochastic Models in Business and Industry, John Wiley & Sons, vol. 32(3), pages 340-353, May.
Handle:
RePEc:wly:apsmbi:v:32:y:2016:i:3:p:340-353
DOI: 10.1002/asmb.2150
Download full text from publisher
Corrections
All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:wly:apsmbi:v:32:y:2016:i:3:p:340-353. See general information about how to correct material in RePEc.
If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.
We have no bibliographic references for this item. You can help adding them by using this form .
If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Wiley Content Delivery (email available below). General contact details of provider: https://doi.org/10.1002/(ISSN)1526-4025 .
Please note that corrections may take a couple of weeks to filter through
the various RePEc services.