11 citations · 14 across the 3 of their papers we have counts for
4 papers · 1 filter
RecGPT: A Foundation Model for Sequential Recommendation
Yangqin Jiang, Xubin Ren, Lianghao Xia +3
This work addresses a fundamental barrier in recommender systems: the inability to generalize across domains without extensive retraining. Traditional ID-based approaches fail enti…
Feature Staleness Aware Incremental Learning for CTR Prediction
Zhikai Wang, Yanyan Shen, Zibin Zhang +1
Click-through Rate (CTR) prediction in real-world recommender systems often deals with billions of user interactions every day. To improve the training efficiency, it is common to…
RecLM: Recommendation Instruction Tuning
Yangqin Jiang, Yuhao Yang, Lianghao Xia +3
Modern recommender systems aim to deeply understand users' complex preferences through their past interactions. While deep collaborative filtering approaches using Graph Neural Net…
RESUS: Warm-Up Cold Users via Meta-Learning Residual User Preferences in CTR Prediction
Yanyan Shen, Lifan Zhao, Weiyu Cheng +3
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,…