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Rejoinder to ‘Estimating rates of rare events through a multidimensional dynamic hierarchical Bayesian framework’

Rejoinder to ‘Estimating rates of rare events through a multidimensional dynamic hierarchical... Rejoinder (wileyonlinelibrary.com) DOI: 10.1002/asmb.2171 Published online in Wiley Online Library Rejoinder to ‘Estimating rates of rare events through a multidimensional dynamic hierarchical Bayesian framework’ We thank Drs. Zhang and Lee, two leading researchers in the area of computational advertisement, for their positive comments, directions to related work, open areas, and insightful questions. Our comments are as follows. Interactions between factors Both discussants comment on the interactions between factors. In the ads application, we have three natural dimensions: publisher (p), advertiser (a), and user (u) with their own hierarchies, respectively. Each forthcoming impression can be related to the three dimensions of different hierarchies, and we propose to decompose the click-through-rate (CTR) q p,a,u of each impression into q q q using tensor decomposition accompanied with shrinkage priors for each hierarchy. Drs. p a u Zhang and Lee both pointed to the paper by Agarwal et al. [1], which provides a framework that works well for modeling pairwise interactions in two-dimensional hierarchies. However, as also noted by Agarwal et al. [1], the direct extension to K-dimensional hierarchies does not perform well because of the increased sparsity. Dr. Lee also pointed to the direction using Gaussian process on tensor factorization. It is also a very interesting direction, and we believe some extra work needs to be developed for the parallel computation that can guarantee convergence in practice. Besides these, the current arising area deep learning [2] should also be a promising direction to handle high-order interactions in our framework. Model tting fi Dr. Zhang also discussed several keys in successfully deploying our framework in practice through Weierstrass Sampler using Spark, especially when there are too few samples falling into some hierarchy combinations of the tuple {p, a, u}. Indeed, we should be extremely careful when deploying through MapReduce. However, we would like to make two points clearer here in the practical implementation: (i) Each CTR prediction is accompanied with a credible interval (CI) estima- tion. When CI is too wide, we will go one level up and use its ‘parents’ CTR prediction. (ii) Because our model fitting involves iterative computations over extremely large datasets, we consider asynchronous iterations for MapReduce simi- larly to [3]. Experiments using different data analysis applications over real-world and synthetic datasets show that using asynchronous iterations for MapReduce performs better than Hadoop for iterative algorithms, reducing execution time of iterative applications by 25% on average. Final comments Again, we thank both the discussants and editors. We hope our paper sheds some lights in dealing with the very challenging CTR prediction problem in the practical deployment. We look forward to future developments in both methodology and applications in the field of the computational advertisement. Hongxia Yang Robert Ormandi Han-Yun Tsao Quan Lu Yahoo! Inc, Sunnyvale, CA 94089, USA E-mail: hy35@stat.duke.edu References 1. Agarwal D, Agrawal R, Khanna R, Kota N. Estimating rates of rare events with multiple hierarchies through scalable log-linear models. Proceedings of the 16th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD ’10. ACM, New York, NY, USA, 2010,213–222. 2. Bengio Y. Learning deep architectures for AI. Foundations and Trends in Machine Learning 2009; 2(1):1–127. 3. Elnikety E, Elsayed T, Ramadan HE. iHadoop: asynchronous iterations for MapReduce. Proceedings of the 2011 IEEE Third International Conference on Cloud Computing Technology and Science, CLOUDCOM ’11. IEEE Computer Society, Washington, DC, USA, 2011,81–90. Copyright © 2016 John Wiley & Sons, Ltd. Appl. Stochastic Models Bus. Ind. 2016, 32 357 http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Applied Stochastic Models in Business and Industry Wiley

Rejoinder to ‘Estimating rates of rare events through a multidimensional dynamic hierarchical Bayesian framework’

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Wiley
Copyright
Copyright © 2016 John Wiley & Sons, Ltd.
ISSN
1524-1904
eISSN
1526-4025
DOI
10.1002/asmb.2171
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Abstract

Rejoinder (wileyonlinelibrary.com) DOI: 10.1002/asmb.2171 Published online in Wiley Online Library Rejoinder to ‘Estimating rates of rare events through a multidimensional dynamic hierarchical Bayesian framework’ We thank Drs. Zhang and Lee, two leading researchers in the area of computational advertisement, for their positive comments, directions to related work, open areas, and insightful questions. Our comments are as follows. Interactions between factors Both discussants comment on the interactions between factors. In the ads application, we have three natural dimensions: publisher (p), advertiser (a), and user (u) with their own hierarchies, respectively. Each forthcoming impression can be related to the three dimensions of different hierarchies, and we propose to decompose the click-through-rate (CTR) q p,a,u of each impression into q q q using tensor decomposition accompanied with shrinkage priors for each hierarchy. Drs. p a u Zhang and Lee both pointed to the paper by Agarwal et al. [1], which provides a framework that works well for modeling pairwise interactions in two-dimensional hierarchies. However, as also noted by Agarwal et al. [1], the direct extension to K-dimensional hierarchies does not perform well because of the increased sparsity. Dr. Lee also pointed to the direction using Gaussian process on tensor factorization. It is also a very interesting direction, and we believe some extra work needs to be developed for the parallel computation that can guarantee convergence in practice. Besides these, the current arising area deep learning [2] should also be a promising direction to handle high-order interactions in our framework. Model tting fi Dr. Zhang also discussed several keys in successfully deploying our framework in practice through Weierstrass Sampler using Spark, especially when there are too few samples falling into some hierarchy combinations of the tuple {p, a, u}. Indeed, we should be extremely careful when deploying through MapReduce. However, we would like to make two points clearer here in the practical implementation: (i) Each CTR prediction is accompanied with a credible interval (CI) estima- tion. When CI is too wide, we will go one level up and use its ‘parents’ CTR prediction. (ii) Because our model fitting involves iterative computations over extremely large datasets, we consider asynchronous iterations for MapReduce simi- larly to [3]. Experiments using different data analysis applications over real-world and synthetic datasets show that using asynchronous iterations for MapReduce performs better than Hadoop for iterative algorithms, reducing execution time of iterative applications by 25% on average. Final comments Again, we thank both the discussants and editors. We hope our paper sheds some lights in dealing with the very challenging CTR prediction problem in the practical deployment. We look forward to future developments in both methodology and applications in the field of the computational advertisement. Hongxia Yang Robert Ormandi Han-Yun Tsao Quan Lu Yahoo! Inc, Sunnyvale, CA 94089, USA E-mail: hy35@stat.duke.edu References 1. Agarwal D, Agrawal R, Khanna R, Kota N. Estimating rates of rare events with multiple hierarchies through scalable log-linear models. Proceedings of the 16th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD ’10. ACM, New York, NY, USA, 2010,213–222. 2. Bengio Y. Learning deep architectures for AI. Foundations and Trends in Machine Learning 2009; 2(1):1–127. 3. Elnikety E, Elsayed T, Ramadan HE. iHadoop: asynchronous iterations for MapReduce. Proceedings of the 2011 IEEE Third International Conference on Cloud Computing Technology and Science, CLOUDCOM ’11. IEEE Computer Society, Washington, DC, USA, 2011,81–90. Copyright © 2016 John Wiley & Sons, Ltd. Appl. Stochastic Models Bus. Ind. 2016, 32 357

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Applied Stochastic Models in Business and Industry – Wiley

Published: May 1, 2016

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