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

!5

▸ 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

!6

▸ Factorization Machine(FM, 因子分解機)

▸ Field-aware Factorization Machine(FFM)

▸ Field-weighted Factorization Machine(FwFM)

Background

!7

Outline

▸ Introduction

▸Method ▸ Experiment

▸ Conclusion

!8

!9

Evolution▸ Logistic Regression model

▸ Degree-2 Polynomial model

!10

Evolution▸ Factorization Machine

▸ Field-aware Factorization Machine(FFM)

!11

Evolution▸ Field-weighted Factorization

Machine(FwFM)

!12

Mutual Information

Outline

▸ Introduction

▸ Method

▸ Experiment ▸ Conclusion

!13

▸Data sets

!14

Experiment

!15

Experiment▸ Comparison of FwFMs with Existing

Models.

!16

Experiment▸ Comparison of FwFMs and FFMs

using the same number of parameters.

▸ L2 Regularization

!17

Experiment

▸ Learning Rate

!18

Experiment

▸ Embedding Vector Dimension

!19

Experiment

▸ Learned field interaction strengths

• For • For • For

!20

Experiment

!21

Experiment

P1356

!22

Experiment

Outline

▸ Introduction

▸ Method

▸ Experiment

▸ Conclusion

!23

Conclusion▸ FwFMs are competitive to FFMs with

significantly less parameters. ▸ FwFMs can indeed learn different

feature interaction strengths from different field pairs.

!24