# Field-aware Factorization Machines in a Real-world Online Advertising System Source: [Field-aware Factorization Machines in a Real-world Online Advertising System]() Collection index: [llms.txt]() Data updated: 2025-10-27T05:52:51.558Z Authors: Yuchin Juan, Damien Lefortier, Olivier Chapelle Published: 2017-01-15T19:13:22.000Z ## Abstract 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. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. - [CTR Prediction on Meitu (test)](): CTR Prediction; Meitu (test); AUC: 0.63. - [CTR Prediction on Avazu](): CTR Prediction; Avazu; AUC: 74. - [Click-Through Rate Prediction on Avazu (test)](): Click-Through Rate Prediction; Avazu (test); AUC: 0.74. ## Original sources and implementation [Original paper]() [PDF]()