7 citations · 23 across the 10 of their papers we have counts for
15 papers
A Generalized Doubly Robust Learning Framework for Debiasing Post-Click Conversion Rate Prediction
Quanyu Dai, Haoxuan Li, Peng Wu +5
Post-click conversion rate (CVR) prediction is an essential task for discovering user interests and increasing platform revenues in a range of industrial applications. One of the m…
Recommendation with User Active Disclosing Willingness
Lei Wang, Xu Chen, Quanyu Dai +1
Recommender system has been deployed in a large amount of real-world applications, profoundly influencing people's daily life and production.Traditional recommender models mostly c…
IntTower: the Next Generation of Two-Tower Model for Pre-Ranking System
Xiangyang Li, Bo Chen, HuiFeng Guo +10
Scoring a large number of candidates precisely in several milliseconds is vital for industrial pre-ranking systems. Existing pre-ranking systems primarily adopt the \textbf{two-tow…
A Brief History of Recommender Systems
Zhenhua Dong, Zhe Wang, Jun Xu +2
Soon after the invention of the Internet, the recommender system emerged and related technologies have been extensively studied and applied by both academia and industry. Currently…
ReLoop: A Self-Correction Continual Learning Loop for Recommender Systems
Guohao Cai, Jieming Zhu, Quanyu Dai +4
Deep learning-based recommendation has become a widely adopted technique in various online applications. Typically, a deployed model undergoes frequent re-training to capture users…
A Semi-Synthetic Dataset Generation Framework for Causal Inference in Recommender Systems
Yan Lyu, Sunhao Dai, Peng Wu +7
Accurate recommendation and reliable explanation are two key issues for modern recommender systems. However, most recommendation benchmarks only concern the prediction of user-item…