[paper review] Field-aware Factorization Machines for CTR prediction

joy5075·2021년 5월 4일
0

1. Introduction

CTR prediction task

2. POLY2, FM, FFM

POLY2

FM

POLY2 vs. FM

FFM (Field-aware Factorization Machines)

3. Implementation

Optimization

Parallelization

FFM data format

Early Stopping

4. Experiment

모델 성능 비교

Reference

  • CTR 예측 알고리즘
    https://brunch.co.kr/@kakao-it/84
  • Vowpal Wabbit
    https://programmers.co.kr/learn/courses/21/lessons/1851
  • PITF(Pairwise Interaction Tensor Factorization for Personalized Tag Recommendation) 논문
    https://analyticsconsultores.com.mx/wp-content/uploads/2019/03/Pairwise-Interaction-Tensor-Factorization-for-Personalized-Tag-Recommendation-Steffen-Rendle-Lars-Schmidt-Thieme-WSDM-2010.pdf
  • negative log-likelihood loss
    https://ratsgo.github.io/deep%20learning/2017/09/24/loss/
  • LM/FM/FFM 수식 설명
    https://teamdable.github.io/techblog/CTR-Prediction-FFM?fbclid=IwAR3ZMCY4qAuRcfr-EnbfBmyGN98u2XSkbaMnnds-8nyJ0Rv7imLsdHP8TVM
  • HOG-WILD! (병렬처리기법)
    https://faculty.ucmerced.edu/frusu/Projects/ScalableGD/scalable-hogwild.html
  • 논문 리뷰 참고
    https://greeksharifa.github.io/machine_learning/2020/04/05/FFM/
    https://dos-tacos.github.io/paper%20review/FFM/

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