18 citations · 22 across the 2 of their papers we have counts for
2 papers
cs.IR2021★ 4 cited
AutoFT: Automatic Fine-Tune for Parameters Transfer Learning in Click-Through Rate Prediction
Xiangli Yang, Qing Liu, Rong Su +3
Recommender systems are often asked to serve multiple recommendation scenarios or domains. Fine-tuning a pre-trained CTR model from source domains and adapting it to a target domai…
cs.IR2020★ 18 cited
U-rank: Utility-oriented Learning to Rank with Implicit Feedback
Xinyi Dai, Jiawei Hou, Qing Liu +6
Learning to rank with implicit feedback is one of the most important tasks in many real-world information systems where the objective is some specific utility, e.g., clicks and rev…