Click-through Rate Prediction with Auto-Quantized Contrastive Learning

Understand

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.