Generative Click-through Rate Prediction with Applications to Search Advertising

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Kong, Lingwei, Wang, Lu, Peng, Changping, Lin, Zhangang, Law, Ching, Shao, Jingping
Format:	Preprint
Published:	2025
Subjects:	Machine Learning
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

_version_	1866909690023116800
author	Kong, Lingwei Wang, Lu Peng, Changping Lin, Zhangang Law, Ching Shao, Jingping
author_facet	Kong, Lingwei Wang, Lu Peng, Changping Lin, Zhangang Law, Ching Shao, Jingping
contents	Click-Through Rate (CTR) prediction models are integral to a myriad of industrial settings, such as personalized search advertising. Current methods typically involve feature extraction from users' historical behavior sequences combined with product information, feeding into a discriminative model that is trained on user feedback to estimate CTR. With the success of models such as GPT, the potential for generative models to enrich expressive power beyond discriminative models has become apparent. In light of this, we introduce a novel model that leverages generative models to enhance the precision of CTR predictions in discriminative models. To reconcile the disparate data aggregation needs of both model types, we design a two-stage training process: 1) Generative pre-training for next-item prediction with the given item category in user behavior sequences; 2) Fine-tuning the well-trained generative model within a discriminative CTR prediction framework. Our method's efficacy is substantiated through extensive experiments on a new dataset, and its significant utility is further corroborated by online A/B testing results. Currently, the model is deployed on one of the world's largest e-commerce platforms, and we intend to release the associated code and dataset in the future.
format	Preprint
id	arxiv_https___arxiv_org_abs_2507_11246
institution	arXiv
publishDate	2025
record_format	arxiv
spellingShingle	Generative Click-through Rate Prediction with Applications to Search Advertising Kong, Lingwei Wang, Lu Peng, Changping Lin, Zhangang Law, Ching Shao, Jingping Machine Learning Click-Through Rate (CTR) prediction models are integral to a myriad of industrial settings, such as personalized search advertising. Current methods typically involve feature extraction from users' historical behavior sequences combined with product information, feeding into a discriminative model that is trained on user feedback to estimate CTR. With the success of models such as GPT, the potential for generative models to enrich expressive power beyond discriminative models has become apparent. In light of this, we introduce a novel model that leverages generative models to enhance the precision of CTR predictions in discriminative models. To reconcile the disparate data aggregation needs of both model types, we design a two-stage training process: 1) Generative pre-training for next-item prediction with the given item category in user behavior sequences; 2) Fine-tuning the well-trained generative model within a discriminative CTR prediction framework. Our method's efficacy is substantiated through extensive experiments on a new dataset, and its significant utility is further corroborated by online A/B testing results. Currently, the model is deployed on one of the world's largest e-commerce platforms, and we intend to release the associated code and dataset in the future.
title	Generative Click-through Rate Prediction with Applications to Search Advertising
topic	Machine Learning
url	https://arxiv.org/abs/2507.11246