Feature Interaction Fusion Self-Distillation Network For CTR Prediction

TL;DR

FSDNet is a novel CTR prediction framework that enhances feature interaction modeling and information sharing through a fusion self-distillation approach, improving performance and generalization across multiple datasets.

Contribution

The paper introduces FSDNet, a new CTR prediction model that integrates explicit and implicit feature interactions with a self-distillation mechanism for better information sharing.

Findings

FSDNet outperforms existing models on four benchmark datasets.

The fusion self-distillation improves feature interaction effectiveness.

The framework demonstrates strong generalization capabilities.

Abstract

Click-Through Rate (CTR) prediction plays a vital role in recommender systems, online advertising, and search engines. Most of the current approaches model feature interactions through stacked or parallel structures, with some employing knowledge distillation for model compression. However, we observe some limitations with these approaches: (1) In parallel structure models, the explicit and implicit components are executed independently and simultaneously, which leads to insufficient information sharing within the feature set. (2) The introduction of knowledge distillation technology brings about the problems of complex teacher-student framework design and low knowledge transfer efficiency. (3) The dataset and the process of constructing high-order feature interactions contain significant noise, which limits the model's effectiveness. To address these limitations, we propose FSDNet, a CTR prediction framework incorporating a plug-and-play fusion self-distillation module. Specifically, FSDNet forms connections between explicit and implicit feature interactions at each layer, enhancing the sharing of information between different features. The deepest fusion layer is then used as the teacher model, utilizing self-distillation to guide the training of shallow layers. Empirical evaluation across four benchmark datasets validates the framework's efficacy and generalization capabilities. The code is available on https://anonymous.4open.science/r/FSDNet.