RecSys 2016: Paper Session 2 - Field Aware Factorization Machines for CTR Prediction
Автор: ACM RecSys
Загружено: 2017-03-30
Просмотров: 8844
Yuchin Juan, Yong Zhuang, Wei-Sheng Chin, Chih-Jen Lin
https://doi.org/10.1145/2959100.2959134
Click-through rate (CTR) prediction plays an important role in computational advertising. Models based on degree-2 polynomial mappings and factorization machines (FMs) are widely used for this task. Recently, a variant of FMs, field-aware factorization machines (FFMs), outperforms existing models in some world-wide CTR-prediction competitions. Based on our experiences in winning two of them, in this paper we establish FFMs as an effective method for classifying large sparse data including those from CTR prediction. First, we propose efficient implementations for training FFMs. Then we comprehensively analyze FFMs and compare this approach with competing models. Experiments show that FFMs are very useful for certain classification problems. Finally, we have released a package of FFMs for public use.
Доступные форматы для скачивания:
Скачать видео mp4
-
Информация по загрузке:
