FinalMLP: An Enhanced Two-Stream MLP Model for CTR Prediction

by   Kelong Mao, et al.

Click-through rate (CTR) prediction is one of the fundamental tasks for online advertising and recommendation. While multi-layer perceptron (MLP) serves as a core component in many deep CTR prediction models, it has been widely recognized that applying a vanilla MLP network alone is inefficient in learning multiplicative feature interactions. As such, many two-stream interaction models (e.g., DeepFM and DCN) have been proposed by integrating an MLP network with another dedicated network for enhanced CTR prediction. As the MLP stream learns feature interactions implicitly, existing research focuses mainly on enhancing explicit feature interactions in the complementary stream. In contrast, our empirical study shows that a well-tuned two-stream MLP model that simply combines two MLPs can even achieve surprisingly good performance, which has never been reported before by existing work. Based on this observation, we further propose feature selection and interaction aggregation layers that can be easily plugged to make an enhanced two-stream MLP model, FinalMLP. In this way, it not only enables differentiated feature inputs but also effectively fuses stream-level interactions across two streams. Our evaluation results on four open benchmark datasets as well as an online A/B test in our industrial system show that FinalMLP achieves better performance than many sophisticated two-stream CTR models. Our source code will be available at MindSpore/models and FuxiCTR/model_zoo.


Feature Interaction based Neural Network for Click-Through Rate Prediction

Click-Through Rate (CTR) prediction is one of the most important and cha...

AIM: Automatic Interaction Machine for Click-Through Rate Prediction

Feature embedding learning and feature interaction modeling are two cruc...

Charge-Based Prison Term Prediction with Deep Gating Network

Judgment prediction for legal cases has attracted much research efforts ...

Online POI Recommendation: Learning Dynamic Geo-Human Interactions in Streams

In this paper, we focus on the problem of modeling dynamic geo-human int...

Dual Graph enhanced Embedding Neural Network for CTR Prediction

CTR prediction, which aims to estimate the probability that a user will ...

A Genetic Feature Selection Based Two-stream Neural Network for Anger Veracity Recognition

People can manipulate emotion expressions when interacting with others. ...

Two-Stream Deep Feature Modelling for Automated Video Endoscopy Data Analysis

Automating the analysis of imagery of the Gastrointestinal (GI) tract ca...

Please sign up or login with your details

Forgot password? Click here to reset