2017/01/15 by Yu-Chin Juan, Juan, Yuchin, Damien Lefortier +3
Computer Science · Decision Sciences · #Advanced Bandit Algorithms Research #FOS: Computer and information sciences #Machine Learning (cs.LG) #Recommender Systems and Techniques #Stochastic Gradient Optimization Techniques
paper · pdf · doi:10.48550/arxiv.1701.04099
openalex publication_date 2017/01/15 · openalex created_date 2019/06/27 · openalex updated_date 2026/07/28
Predicting user response is one of the core machine learning tasks in computational advertising. Field-aware Factorization Machines (FFM) have recently been established as a state-of-the-art method for that problem and in particular won two Kaggle challenges. This paper presents some results from implementing this method in a production system that predicts click-through and conversion rates for display advertising and shows that this method it is not only effective to win challenges but is also valuable in a real-world prediction system. We also discuss some specific challenges and solutions to reduce the training time, namely the use of an innovative seeding algorithm and a distributed learning mechanism.