Click-through Rate Prediction with Auto-Quantized Contrastive Learning
This addresses cold-start issues in web recommendation systems, though it appears incremental as it builds on existing CTR models.
The paper tackles the problem of click-through rate prediction under cold-start scenarios with sparse user interactions by proposing an Auto-Quantized Contrastive Learning loss, which consistently improves state-of-the-art CTR models.
Click-through rate (CTR) prediction becomes indispensable in ubiquitous web recommendation applications. Nevertheless, the current methods are struggling under the cold-start scenarios where the user interactions are extremely sparse. We consider this problem as an automatic identification about whether the user behaviors are rich enough to capture the interests for prediction, and propose an Auto-Quantized Contrastive Learning (AQCL) loss to regularize the model. Different from previous methods, AQCL explores both the instance-instance and the instance-cluster similarity to robustify the latent representation, and automatically reduces the information loss to the active users due to the quantization. The proposed framework is agnostic to different model architectures and can be trained in an end-to-end fashion. Extensive results show that it consistently improves the current state-of-the-art CTR models.