Field-weighted Factorization Machines for Click-Through Rate Prediction in Display Advertising
Author : Junwei Pan, Jian Xu, Alfonso Lobos Ruiz, Wenliang Zhao, Shengjun Pan, Yu Sun, Quan Lu Source : WWW’ 18 Advisor : Jia-Ling Koh Speaker : Chia-Yi Huang Date : 2018/05/22
Outline
▸ Introduction ▸ Method
▸ Experiment
▸ Conclusion
!2
▸Goal • Display ad CTR prediction use Field-
weighted Factorization Machines
Introduction
!3
▸Goal • Display ad CTR prediction use Field-
weighted Factorization Machines
Introduction
!4
▸ Field & Feature
Introduction
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▸ Feature interactions are prevalent and need to be specifically modeled.
▸ Features from one field often interact differently with features from different other fields.
▸ Potentially high model complexity needs to be taken care of.
Challenge
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▸ Factorization Machine(FM, 因子分解機)
▸ Field-aware Factorization Machine(FFM)
▸ Field-weighted Factorization Machine(FwFM)
Background
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Outline
▸ Introduction
▸Method ▸ Experiment
▸ Conclusion
!8
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Evolution▸ Logistic Regression model
▸ Degree-2 Polynomial model
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Evolution▸ Factorization Machine
▸ Field-aware Factorization Machine(FFM)
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Evolution▸ Field-weighted Factorization
Machine(FwFM)
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Mutual Information
Outline
▸ Introduction
▸ Method
▸ Experiment ▸ Conclusion
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▸Data sets
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Experiment
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Experiment▸ Comparison of FwFMs with Existing
Models.
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Experiment▸ Comparison of FwFMs and FFMs
using the same number of parameters.
▸ L2 Regularization
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Experiment
▸ Learning Rate
!18
Experiment
▸ Embedding Vector Dimension
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Experiment
▸ Learned field interaction strengths
• For • For • For
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Experiment
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Experiment
P1356
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Experiment
Outline
▸ Introduction
▸ Method
▸ Experiment
▸ Conclusion
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Conclusion▸ FwFMs are competitive to FFMs with
significantly less parameters. ▸ FwFMs can indeed learn different
feature interaction strengths from different field pairs.
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