FinalMLP: An En… (2023-04-03T00:00:00.000000Z)

TL;DR

This paper presents a simple two-stream feature interaction model, namely FinalMLP, which employs only MLPs in both streams yet achieves surprisingly strong performance and could serve as a new strong baseline for future development of two- stream CTR models.

Abstract

Click-through rate (CTR) prediction is one of the fundamental tasks in online advertising and recommendation. Multi-layer perceptron (MLP) serves as a core component in many deep CTR prediction models, but it has been widely shown that applying a vanilla MLP network alone is ineffective in learning complex feature interactions. As such, many two-stream models (e.g., Wide&Deep, DeepFM, and DCN) have recently been proposed, aiming to integrate two parallel sub-networks to learn feature interactions from two different views for enhanced CTR prediction. In addition to one MLP stream that learns feature interactions implicitly, most of the existing research focuses on designing another stream to complement the MLP stream with explicitly enhanced feature interactions. Instead, this paper presents a simple two-stream feature interaction model, namely FinalMLP, which employs only MLPs in both streams yet achieves surprisingly strong performance. In contrast to sophisticated network design in each stream, our work enhances CTR modeling through a feature selection module, which produces differentiated feature inputs to two streams, and a group-wise bilinear fusion module, which effectively captures stream-level interactions across two streams. We show that FinalMLP achieves competitive or even better performance against many existing two-stream CTR models on four open benchmark datasets and also brings significant CTR improvements during an online A/B test in our industrial news recommender system. We envision that the simple yet effective FinalMLP model could serve as a new strong baseline for future development of two-stream CTR models. Our source code will be available at MindSpore/models and FuxiCTR/model_zoo.

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Abstract

References32 items

BARS: Towards Open Benchmarking for Recommender Systems

Enhanced Exploration in Neural Feature Selection for Deep Click-Through Rate Prediction Models via Ensemble of Gating Layers

Enhancing Explicit and Implicit Feature Interactions via Information Sharing for Parallel Deep CTR Models

Looking at CTR Prediction Again: Is Attention All You Need?

Deep Learning for Click-Through Rate Estimation

FM2: Field-matrixed Factorization Machines for Recommender Systems

MaskNet: Introducing Feature-Wise Multiplication to CTR Ranking Models by Instance-Guided Mask

Deep Interaction Machine: A Simple but Effective Model for High-order Feature Interactions

Open Benchmarking for Click-Through Rate Prediction

DCN V2: Improved Deep & Cross Network and Practical Lessons for Web-scale Learning to Rank Systems

Neural Collaborative Filtering vs. Matrix Factorization Revisited

Fi-GNN: Modeling Feature Interactions via Graph Neural Networks for CTR Prediction

Adaptive Factorization Network: Learning Adaptive-Order Feature Interactions

FiBiNET: combining feature importance and bilinear feature interaction for click-through rate prediction

AutoInt: Automatic Feature Interaction Learning via Self-Attentive Neural Networks

Modeling Task Relationships in Multi-task Learning with Multi-gate Mixture-of-Experts

Field-weighted Factorization Machines for Click-Through Rate Prediction in Display Advertising

xDeepFM: Combining Explicit and Implicit Feature Interactions for Recommender Systems

Deep & Cross Network for Ad Click Predictions

Attentional Factorization Machines: Learning the Weight of Feature Interactions via Attention Networks

TL;DR

Abstract

Authors

TL;DR

Abstract

Authors

References32 items

BARS: Towards Open Benchmarking for Recommender Systems

Enhanced Exploration in Neural Feature Selection for Deep Click-Through Rate Prediction Models via Ensemble of Gating Layers

Enhancing Explicit and Implicit Feature Interactions via Information Sharing for Parallel Deep CTR Models

Looking at CTR Prediction Again: Is Attention All You Need?

Deep Learning for Click-Through Rate Estimation

FM2: Field-matrixed Factorization Machines for Recommender Systems

MaskNet: Introducing Feature-Wise Multiplication to CTR Ranking Models by Instance-Guided Mask

Deep Interaction Machine: A Simple but Effective Model for High-order Feature Interactions

Open Benchmarking for Click-Through Rate Prediction

DCN V2: Improved Deep & Cross Network and Practical Lessons for Web-scale Learning to Rank Systems

Neural Collaborative Filtering vs. Matrix Factorization Revisited

Fi-GNN: Modeling Feature Interactions via Graph Neural Networks for CTR Prediction

Adaptive Factorization Network: Learning Adaptive-Order Feature Interactions

FiBiNET: combining feature importance and bilinear feature interaction for click-through rate prediction

AutoInt: Automatic Feature Interaction Learning via Self-Attentive Neural Networks

Modeling Task Relationships in Multi-task Learning with Multi-gate Mixture-of-Experts

Field-weighted Factorization Machines for Click-Through Rate Prediction in Display Advertising

xDeepFM: Combining Explicit and Implicit Feature Interactions for Recommender Systems

Deep & Cross Network for Ad Click Predictions

Attentional Factorization Machines: Learning the Weight of Feature Interactions via Attention Networks

Attention is All you Need

DeepFM: A Factorization-Machine based Neural Network for CTR Prediction

Factorized Bilinear Models for Image Recognition

Feature Selection Methods Evaluation for CTR Estimation

Field-aware Factorization Machines for CTR Prediction

Deep Neural Networks for YouTube Recommendations

Higher-Order Factorization Machines

Wide & Deep Learning for Recommender Systems

Bilinear CNN Models for Fine-Grained Visual Recognition

Practical Lessons from Predicting Clicks on Ads at Facebook

Factorization Machines

Predicting clicks: estimating the click-through rate for new ads

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