User Representation

User representation learning aims to capture user preferences and behaviors in compact vector representations, primarily for improved personalization in applications like recommendation systems and advertising. Current research emphasizes creating robust and generalized representations that adapt to diverse user segments and tasks, often employing techniques like contrastive learning, transformer models, and mixture-of-experts architectures to handle varied data modalities and address data sparsity. These advancements are significant because effective user representations are crucial for enhancing the accuracy, efficiency, and personalization capabilities of numerous online services, while also raising important privacy considerations regarding re-identification risk.