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TF4CTR: Twin Focus Framework for CTR Prediction via Adaptive Sample Differentiation

Honghao Li, Yiwen Zhang, Yi Zhang, Lei Sang, Yun Yang

TL;DR

TF4CTR addresses the lack of sample differentiation and uniform supervision in parallel-structured CTR models by introducing a lightweight, plug-and-play framework that differentiates samples at the embedding level, applies a target-specific Twin Focus Loss, and dynamically fuses outputs from simple and complex feature interaction encoders. The Sample Selection Embedding Module (SSEM), Differentiated FI Encoders, Twin Focus Loss, and Dynamic Fusion Module (DFM) work together to improve generalization and prediction accuracy across five real-world datasets, in a model-agnostic manner. Theoretical analysis shows TF4CTR induces differentiated gradient signals for encoders, and empirical results demonstrate consistent AUC gains over strong baselines with favorable time/space overhead. The framework is open-sourced and compatible with popular CTR architectures, offering practical benefits for industrial recommender systems and robust handling of varying sample difficulty.

Abstract

Effective feature interaction modeling is critical for enhancing the accuracy of click-through rate (CTR) prediction in industrial recommender systems. Most of the current deep CTR models resort to building complex network architectures to better capture intricate feature interactions or user behaviors. However, we identify two limitations in these models: (1) the samples given to the model are undifferentiated, which may lead the model to learn a larger number of easy samples in a single-minded manner while ignoring a smaller number of hard samples, thus reducing the model's generalization ability; (2) differentiated feature interaction encoders are designed to capture different interactions information but receive consistent supervision signals, thereby limiting the effectiveness of the encoder. To bridge the identified gaps, this paper introduces a novel CTR prediction framework by integrating the plug-and-play Twin Focus (TF) Loss, Sample Selection Embedding Module (SSEM), and Dynamic Fusion Module (DFM), named the Twin Focus Framework for CTR (TF4CTR). Specifically, the framework employs the SSEM at the bottom of the model to differentiate between samples, thereby assigning a more suitable encoder for each sample. Meanwhile, the TF Loss provides tailored supervision signals to both simple and complex encoders. Moreover, the DFM dynamically fuses the feature interaction information captured by the encoders, resulting in more accurate predictions. Experiments on five real-world datasets confirm the effectiveness and compatibility of the framework, demonstrating its capacity to enhance various representative baselines in a model-agnostic manner. To facilitate reproducible research, our open-sourced code and detailed running logs will be made available at: https://github.com/salmon1802/TF4CTR.

TF4CTR: Twin Focus Framework for CTR Prediction via Adaptive Sample Differentiation

TL;DR

TF4CTR addresses the lack of sample differentiation and uniform supervision in parallel-structured CTR models by introducing a lightweight, plug-and-play framework that differentiates samples at the embedding level, applies a target-specific Twin Focus Loss, and dynamically fuses outputs from simple and complex feature interaction encoders. The Sample Selection Embedding Module (SSEM), Differentiated FI Encoders, Twin Focus Loss, and Dynamic Fusion Module (DFM) work together to improve generalization and prediction accuracy across five real-world datasets, in a model-agnostic manner. Theoretical analysis shows TF4CTR induces differentiated gradient signals for encoders, and empirical results demonstrate consistent AUC gains over strong baselines with favorable time/space overhead. The framework is open-sourced and compatible with popular CTR architectures, offering practical benefits for industrial recommender systems and robust handling of varying sample difficulty.

Abstract

Effective feature interaction modeling is critical for enhancing the accuracy of click-through rate (CTR) prediction in industrial recommender systems. Most of the current deep CTR models resort to building complex network architectures to better capture intricate feature interactions or user behaviors. However, we identify two limitations in these models: (1) the samples given to the model are undifferentiated, which may lead the model to learn a larger number of easy samples in a single-minded manner while ignoring a smaller number of hard samples, thus reducing the model's generalization ability; (2) differentiated feature interaction encoders are designed to capture different interactions information but receive consistent supervision signals, thereby limiting the effectiveness of the encoder. To bridge the identified gaps, this paper introduces a novel CTR prediction framework by integrating the plug-and-play Twin Focus (TF) Loss, Sample Selection Embedding Module (SSEM), and Dynamic Fusion Module (DFM), named the Twin Focus Framework for CTR (TF4CTR). Specifically, the framework employs the SSEM at the bottom of the model to differentiate between samples, thereby assigning a more suitable encoder for each sample. Meanwhile, the TF Loss provides tailored supervision signals to both simple and complex encoders. Moreover, the DFM dynamically fuses the feature interaction information captured by the encoders, resulting in more accurate predictions. Experiments on five real-world datasets confirm the effectiveness and compatibility of the framework, demonstrating its capacity to enhance various representative baselines in a model-agnostic manner. To facilitate reproducible research, our open-sourced code and detailed running logs will be made available at: https://github.com/salmon1802/TF4CTR.
Paper Structure (37 sections, 10 equations, 11 figures, 7 tables, 1 algorithm)

This paper contains 37 sections, 10 equations, 11 figures, 7 tables, 1 algorithm.

Table of Contents

  1. Introduction
  2. Preliminaries
  3. CTR Prediction Task
  4. Three Categories of Samples
  5. Proposed Method
  6. Twin Focus Framework
  7. Embedding Layer
  8. Sample Selection Embedding Module
  9. Differentiated FI Encoder
  10. Twin Focus Loss Function
  11. Dynamic Fusion Module
  12. Multi-task Training
  13. Theoretical Analysis
  14. Gradient of $\mathcal{L}_{ctr}$ for Encoders
  15. Gradient of $\mathcal{L}_{TF}$ for Encoders
  16. ...and 22 more sections

Figures (11)

  • Figure 1: The workflow of current parallel-structured CTR models
  • Figure 2: Distribution of DNN's prediction results for positive and negative samples on training and validation datasets. Red indicates misclassified, blue indicates poorly classified, and green indicates well-classified.
  • Figure 3: An example of a three-category sample.
  • Figure 4: The workflow of TF4CTR.
  • Figure 5: Visualization of $\mathcal{L}_{T F}$ for various hyperparameters
  • ...and 6 more figures