A Dual Input-aware Factorization Machine for CTR Prediction

Code

• github.com/shenweichen/DeepCTR
  tf★ 8,006
• github.com/shenweichen/DeepCTR-Torch
  pytorch★ 3,394
• github.com/UlionTse/mlgb
  pytorch★ 1,049
• github.com/Andy1314Chen/DIFM-Paddle
  paddle★ 1
• github.com/LinJayan/DIFM_Paddle
  paddle★ 0
• github.com/PaddlePaddle/PaddleRec/tree/master/models/rank/difm
  paddle★ 0

Abstract

Factorization Machines (FMs) refer to a class of general predictors working with real valued feature vectors, which are well-known for their ability to estimate model parameters under significant sparsity and have found successful applications in many areas such as the click-through rate (CTR) prediction. However, standard FMs only produce a single fixed representation for each feature across different input instances, which may limit the CTR model’s expressive and predictive power. Inspired by the success of Input-aware Factorization Machines (IFMs), which aim to learn more flexible and informative representations of a given feature according to different input instances, we propose a novel model named Dual Input-aware Factorization Machines (DIFMs) that can adaptively reweight the original feature representations at the bit-wise and vector-wise levels simultaneously. Furthermore, DIFMs strategically integrate various components including Multi-Head Self-Attention, Residual Networks and DNNs into a unified end-to-end model. Comprehensive experiments on two real-world CTR prediction datasets show that the DIFM model can outperform several state-of-the-art models consistently.

Tasks

Reproductions