Paper Info
Title
Field-aware Factorization Machines for CTR Prediction.
Abstract
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.
Year
DOI
Venue
2016
10.1145/2959100.2959134
RecSys
Keywords
Field
DocType
Machine learning, Click-through rate prediction, Computational advertising, Factorization machines
Data mining,Polynomial,Effective method,Computer science,Computational advertising,Implementation,Theoretical computer science,Artificial intelligence,Factorization,Machine learning,Sparse matrix
Conference
Citations 
PageRank 
References 
87
2.63
13
Authors
4
Name
Order
Citations
PageRank
Yu-Chin Juan12529.54
Yong Zhuang225413.88
Wei-Sheng Chin32368.76
Chih-Jen Lin4202861475.84