| Autor: |
Juan, Yuchin, Lefortier, Damien, Chapelle, Olivier |
| Rok vydání: |
2017 |
| Předmět: |
|
| Druh dokumentu: |
Working Paper |
| Popis: |
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. |
| Databáze: |
arXiv |
| Externí odkaz: |
|
