MARS: Modality-Aligned Retrieval for Sequence Augmented CTR Prediction

Understand

Click-through rate (CTR) prediction serves as a cornerstone of recommender systems.

• Despite the strong performance of current CTR models based on user behavior modeling, they are still severely limited by interaction sparsity, especially in low-active user scenarios.
• To address this issue, data augmentation of user behavior is a promising research direction.
• However, existing data augmentation methods heavily rely on collaborative signals while overlooking the rich multimodal features of items, leading to insufficient modeling of low-active users.