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

Abstract

Click-through rate (CTR) prediction is a critical task in online display advertising. The data involved in CTR prediction are typically multi-field categorical data, i.e., every feature is categorical and belongs to one and only one field. One of the interesting characteristics of such data is that features from one field often interact differently with features from different other fields. Recently, Field-aware Factorization Machines (FFMs) have been among the best performing models for CTR prediction by explicitly modeling such difference. However, the number of parameters in FFMs is in the order of feature number times field number, which is unacceptable in the real-world production systems. In this paper, we propose Field-weighted Factorization Machines (FwFMs) to model the different feature interactions between different fields in a much more memory-efficient way. Our experimental evaluations show that FwFMs can achieve competitive prediction performance with only as few as 4% parameters of FFMs. When using the same number of parameters, FwFMs can bring 0.92% and 0.47% AUC lift over FFMs on two real CTR prediction data sets.

Cited by in corpus (27)

• Click-Through Rate Prediction in Online Advertising: A Literature Review
• $F{M}^2$: Field-matrixed Factorization Machines for Recommender Systems
• CL4CTR: A Contrastive Learning Framework for CTR Prediction
• EulerNet: Adaptive Feature Interaction Learning via Euler's Formula for CTR Prediction
• Deep Spatio-Temporal Neural Networks for Click-Through Rate Prediction
• Enhancing CTR Prediction with Context-Aware Feature Representation Learning
• Predicting Different Types of Conversions with Multi-Task Learning in Online Advertising
• An Efficient Deep Distribution Network for Bid Shading in First-Price Auctions
• Directed Acyclic Graph Factorization Machines for CTR Prediction via Knowledge Distillation
• Bid Shading in The Brave New World of First-Price Auctions
• Ads Recommendation in a Collapsed and Entangled World
• Reformulating CTR Prediction: Learning Invariant Feature Interactions for Recommendation
• Looking at CTR Prediction Again: Is Attention All You Need?
• HIEN: Hierarchical Intention Embedding Network for Click-Through Rate Prediction
• DCAP: Deep Cross Attentional Product Network for User Response Prediction
• Automated Creative Optimization for E-Commerce Advertising
• Understanding the Ranking Loss for Recommendation with Sparse User Feedback
• Temporal Interest Network for User Response Prediction
• Unbiased Filtering Of Accidental Clicks in Verizon Media Native Advertising
• Risk-Aware Bid Optimization for Online Display Advertisement
• DLF: Enhancing Explicit-Implicit Interaction via Dynamic Low-Order-Aware Fusion for CTR Prediction
• FedUD: Exploiting Unaligned Data for Cross-Platform Federated Click-Through Rate Prediction
• ENCODE: Breaking the Trade-Off Between Performance and Efficiency in Long-Term User Behavior Modeling
• ELEC: Efficient Large Language Model-Empowered Click-Through Rate Prediction
• Toward a benchmark for CTR prediction in online advertising: datasets, evaluation protocols and perspectives
• MemoNet: Memorizing All Cross Features' Representations Efficiently via Multi-Hash Codebook Network for CTR Prediction
• Dual-Stream MLP is All You Need for CTR Prediction