Large Scale CVR Prediction through Dynamic Transfer Learning of Global and Local Features

Hongxia Yang, Quan Lu, Angus Xianen Qiu, Chun Han
Proceedings of the 5th International Workshop on Big Data, Streams and Heterogeneous Source Mining: Algorithms, Systems, Programming Models and Applications at KDD 2016, PMLR 53:103-119, 2016.

Abstract

This paper presents a combination of strategies for conversion rate (CVR) prediction de- ployed at the Yahoo! demand side platform (DSP) Brightroll, targeting at modeling extremely high dimensional, sparse data with limited human intervention. We propose a novel probabilistic generative model by tightly integrating components of natural language processing, dynamic transfer learning and scalable prediction, named Dynamic Transfer Learning with Reinforced Word Modeling (a.k.a. Trans-RWM ) to predict user conversion rates. Our model is based on assumptions that: on a higher level, information can be transferable between related campaigns; on a lower level, users who searched similar contents or browsed similar pages would have a higher probability of sharing similar latent purchase interests. Novelties of this framework include (i) A novel natural language modeling specifically tailored for semantic inputs of CVR prediction; (ii) A Bayesian transfer learning model to dynamically transfer the knowledge from source to the future target; (iii) An automatic new updating rule with adaptive regularization using Stochastic Gradient Monte Carlo to support the efficient updating of Trans-RWM in high-dimensional and sparse data. We demonstrate that on Brightroll our framework can effectively discriminate extremely rare events in terms of their conversion propensity.

Cite this Paper

BibTeX
@InProceedings{pmlr-v53-yang16, title = {Large Scale CVR Prediction through Dynamic Transfer Learning of Global and Local Features}, author = {Yang, Hongxia and Lu, Quan and Xianen Qiu, Angus and Han, Chun}, booktitle = {Proceedings of the 5th International Workshop on Big Data, Streams and Heterogeneous Source Mining: Algorithms, Systems, Programming Models and Applications at KDD 2016}, pages = {103--119}, year = {2016}, editor = {Fan, Wei and Bifet, Albert and Read, Jesse and Yang, Qiang and Yu, Philip S.}, volume = {53}, series = {Proceedings of Machine Learning Research}, address = {San Francisco, California, USA}, month = {14 Aug}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v53/yang16.pdf}, url = {https://proceedings.mlr.press/v53/yang16.html}, abstract = {This paper presents a combination of strategies for conversion rate (CVR) prediction de- ployed at the Yahoo! demand side platform (DSP) Brightroll, targeting at modeling extremely high dimensional, sparse data with limited human intervention. We propose a novel probabilistic generative model by tightly integrating components of natural language processing, dynamic transfer learning and scalable prediction, named Dynamic Transfer Learning with Reinforced Word Modeling (a.k.a. Trans-RWM ) to predict user conversion rates. Our model is based on assumptions that: on a higher level, information can be transferable between related campaigns; on a lower level, users who searched similar contents or browsed similar pages would have a higher probability of sharing similar latent purchase interests. Novelties of this framework include (i) A novel natural language modeling specifically tailored for semantic inputs of CVR prediction; (ii) A Bayesian transfer learning model to dynamically transfer the knowledge from source to the future target; (iii) An automatic new updating rule with adaptive regularization using Stochastic Gradient Monte Carlo to support the efficient updating of Trans-RWM in high-dimensional and sparse data. We demonstrate that on Brightroll our framework can effectively discriminate extremely rare events in terms of their conversion propensity.} }
Endnote
%0 Conference Paper %T Large Scale CVR Prediction through Dynamic Transfer Learning of Global and Local Features %A Hongxia Yang %A Quan Lu %A Angus Xianen Qiu %A Chun Han %B Proceedings of the 5th International Workshop on Big Data, Streams and Heterogeneous Source Mining: Algorithms, Systems, Programming Models and Applications at KDD 2016 %C Proceedings of Machine Learning Research %D 2016 %E Wei Fan %E Albert Bifet %E Jesse Read %E Qiang Yang %E Philip S. Yu %F pmlr-v53-yang16 %I PMLR %P 103--119 %U https://proceedings.mlr.press/v53/yang16.html %V 53 %X This paper presents a combination of strategies for conversion rate (CVR) prediction de- ployed at the Yahoo! demand side platform (DSP) Brightroll, targeting at modeling extremely high dimensional, sparse data with limited human intervention. We propose a novel probabilistic generative model by tightly integrating components of natural language processing, dynamic transfer learning and scalable prediction, named Dynamic Transfer Learning with Reinforced Word Modeling (a.k.a. Trans-RWM ) to predict user conversion rates. Our model is based on assumptions that: on a higher level, information can be transferable between related campaigns; on a lower level, users who searched similar contents or browsed similar pages would have a higher probability of sharing similar latent purchase interests. Novelties of this framework include (i) A novel natural language modeling specifically tailored for semantic inputs of CVR prediction; (ii) A Bayesian transfer learning model to dynamically transfer the knowledge from source to the future target; (iii) An automatic new updating rule with adaptive regularization using Stochastic Gradient Monte Carlo to support the efficient updating of Trans-RWM in high-dimensional and sparse data. We demonstrate that on Brightroll our framework can effectively discriminate extremely rare events in terms of their conversion propensity.
RIS
TY - CPAPER TI - Large Scale CVR Prediction through Dynamic Transfer Learning of Global and Local Features AU - Hongxia Yang AU - Quan Lu AU - Angus Xianen Qiu AU - Chun Han BT - Proceedings of the 5th International Workshop on Big Data, Streams and Heterogeneous Source Mining: Algorithms, Systems, Programming Models and Applications at KDD 2016 DA - 2016/12/06 ED - Wei Fan ED - Albert Bifet ED - Jesse Read ED - Qiang Yang ED - Philip S. Yu ID - pmlr-v53-yang16 PB - PMLR DP - Proceedings of Machine Learning Research VL - 53 SP - 103 EP - 119 L1 - http://proceedings.mlr.press/v53/yang16.pdf UR - https://proceedings.mlr.press/v53/yang16.html AB - This paper presents a combination of strategies for conversion rate (CVR) prediction de- ployed at the Yahoo! demand side platform (DSP) Brightroll, targeting at modeling extremely high dimensional, sparse data with limited human intervention. We propose a novel probabilistic generative model by tightly integrating components of natural language processing, dynamic transfer learning and scalable prediction, named Dynamic Transfer Learning with Reinforced Word Modeling (a.k.a. Trans-RWM ) to predict user conversion rates. Our model is based on assumptions that: on a higher level, information can be transferable between related campaigns; on a lower level, users who searched similar contents or browsed similar pages would have a higher probability of sharing similar latent purchase interests. Novelties of this framework include (i) A novel natural language modeling specifically tailored for semantic inputs of CVR prediction; (ii) A Bayesian transfer learning model to dynamically transfer the knowledge from source to the future target; (iii) An automatic new updating rule with adaptive regularization using Stochastic Gradient Monte Carlo to support the efficient updating of Trans-RWM in high-dimensional and sparse data. We demonstrate that on Brightroll our framework can effectively discriminate extremely rare events in terms of their conversion propensity. ER -
APA
Yang, H., Lu, Q., Xianen Qiu, A. & Han, C.. (2016). Large Scale CVR Prediction through Dynamic Transfer Learning of Global and Local Features. Proceedings of the 5th International Workshop on Big Data, Streams and Heterogeneous Source Mining: Algorithms, Systems, Programming Models and Applications at KDD 2016, in Proceedings of Machine Learning Research 53:103-119 Available from https://proceedings.mlr.press/v53/yang16.html.

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