Infer As You Train: A Symmetric Paradigm of Masked Generative for Click-Through Rate Prediction

TL;DR

This paper introduces SGCTR, a symmetric masked generative framework for CTR prediction that leverages generative capabilities during both training and inference, leading to improved accuracy.

Contribution

It proposes a novel symmetric paradigm that applies generative modeling during inference, unlike prior methods that only used it during training.

Findings

SGCTR outperforms existing CTR models in experiments.

Applying generative capabilities during inference enhances prediction accuracy.

The symmetric paradigm effectively mitigates noisy feature impacts.

Abstract

Generative models are increasingly being explored in click-through rate (CTR) prediction field to overcome the limitations of the conventional discriminative paradigm, which rely on a simple binary classification objective. However, existing generative models typically confine the generative paradigm to the training phase, primarily for representation learning. During online inference, they revert to a standard discriminative paradigm, failing to leverage their powerful generative capabilities to further improve prediction accuracy. This fundamental asymmetry between the training and inference phases prevents the generative paradigm from realizing its full potential. To address this limitation, we propose the Symmetric Masked Generative Paradigm for CTR prediction (SGCTR), a novel framework that establishes symmetry between the training and inference phases. Specifically, after acquiring generative capabilities by learning feature dependencies during training, SGCTR applies the generative capabilities during online inference to iteratively redefine the features of input samples, which mitigates the impact of noisy features and enhances prediction accuracy. Extensive experiments validate the superiority of SGCTR, demonstrating that applying the generative paradigm symmetrically across both training and inference significantly unlocks its power in CTR prediction.