RESEARCH

Practical Lessons from Predicting Clicks on Ads at Facebook

August 24, 2014

Abstract

Online advertising allows advertisers to only bid and pay for measurable user responses, such as clicks on ads. As a consequence, click prediction systems are central to most online advertising systems. With over 750 million daily active users and over 1 million active advertisers, predicting clicks on Facebook ads is a challenging machine learning task. In this paper we introduce a model which combines decision trees with logistic regression, outperforming either of these methods on its own by over 3%, an improvement with significant impact to the overall system performance. We then explore how a number of fundamental parameters impact the final prediction performance of our system. Not surprisingly, the most important thing is to have the right features: those capturing historical information about the user or ad dominate other types of features. Once we have the right features and the right model (decisions trees plus logistic regression), other factors play small roles (though even small improvements are important at scale). Picking the optimal handling for data freshness, learning rate schema and data sampling improve the model slightly, though much less than adding a high-value feature, or picking the right model to begin with.

Download the Paper

Related Publications

June 14, 2020

Iterative Answer Prediction with Pointer-Augmented Multimodal Transformers for TextVQA | Facebook AI Research

Ronghang Hu, Amanpreet Singh, Trevor Darrell, Marcus Rohrbach

June 14, 2020

June 17, 2019

COMPUTER VISION

DMC-Net: Generating Discriminative Motion Cues for Fast Compressed Video Action Recognition | Facebook AI Research

Zheng Shou, Xudong Lin, Yannis Kalantidis, Laura Sevilla-Lara, Marcus Rohrbach, Shih-Fu Chang, Zhicheng Yan

June 17, 2019

June 18, 2019

COMPUTER VISION

Embodied Question Answering in Photorealistic Environments with Point Cloud Perception | Facebook AI Research

Erik Wijmans, Samyak Datta, Oleksandr Maksymets, Abhishek Das, Georgia Gkioxari, Stefan Lee, Irfan Essa, Devi Parikh, Dhruv Batra

June 18, 2019

August 01, 2019

NLP

Simple and Effective Curriculum Pointer-Generator Networks for Reading Comprehension over Long Narratives | Facebook AI Research

Yi Tay, Shuohang Wang, Luu Anh Tuan, Jie Fu, Minh C. Phan, Xingdi Yuan, Jinfeng Rao, Siu Cheung Hui, Aston Zhang

August 01, 2019

Help Us Pioneer The Future of AI

We share our open source frameworks, tools, libraries, and models for everything from research exploration to large-scale production deployment.