DeepGen: Diverse Search Ad Generation and Real-Time Customization
Konstantin Golobokov, Junyi Chai, Victor Ye Dong, Mandy Gu, Bingyu, Chi, Jie Cao, Yulan Yan, Yi Liu

TL;DR
DeepGen is a scalable system that automatically generates and customizes diverse, fluent search ads in real-time using advanced NLG models, improving relevance and diversity for BingAds.
Contribution
The paper introduces DeepGen, a novel system combining diverse, controllable NLG and real-time customization for search ads, addressing factuality, speed, and diversity challenges.
Findings
System serves approximately 4% of Bing's global ads.
Generated ads show increased diversity and relevance.
System effectively balances diversity and inference speed.
Abstract
We present DeepGen, a system deployed at web scale for automatically creating sponsored search advertisements (ads) for BingAds customers. We leverage state-of-the-art natural language generation (NLG) models to generate fluent ads from advertiser's web pages in an abstractive fashion and solve practical issues such as factuality and inference speed. In addition, our system creates a customized ad in real-time in response to the user's search query, therefore highlighting different aspects of the same product based on what the user is looking for. To achieve this, our system generates a diverse choice of smaller pieces of the ad ahead of time and, at query time, selects the most relevant ones to be stitched into a complete ad. We improve generation diversity by training a controllable NLG model to generate multiple ads for the same web page highlighting different selling points. Our…
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Taxonomy
TopicsConsumer Market Behavior and Pricing · Recommender Systems and Techniques · Digital Marketing and Social Media
