Kangyi Lin

RESUS: Warm-up Cold Users via Meta-learning Residual User Preferences in CTR Prediction

2022 · ACM Transactions on Information Systems

Click-through Rate (CTR) prediction on cold users is a challenging task in recommender systems. Recent researches have resorted to meta-learning to tackle the cold-user challenge, which either perform few-shot user representation learning or adopt optimization-based …

Automated Self-Supervised Learning for Recommendation

2023

Graph neural networks (GNNs) have emerged as the state-of-the-art paradigm for collaborative filtering (CF). To improve the representation quality over limited labeled data, contrastive learning has attracted attention in recommendation and benefited graph-based CF model …