5 papers
Incentivized Exploration with Stochastic Covariates: A Two-Stage Mechanism Design for Recommender System
Yuantong Li, Guang Cheng, Xiaowu Dai
Recommender systems play a crucial role in internet economies by connecting users with relevant products. However, designing effective recommender systems faces the key challenges:…
MBD: A Model-Based Debiasing Framework Across User, Content, and Model Dimensions
Yuantong Li, Lei Yuan, Zhihao Zheng +27
Modern recommendation systems rank candidates by aggregating multiple behavioral signals through a value model. However, many commonly used signals are inherently affected by heter…
Epinet for Content Cold Start
Hong Jun Jeon, Songbin Liu, Yuantong Li +5
The exploding popularity of online content and its user base poses an evermore challenging matching problem for modern recommendation systems. Unlike other frontiers of machine lea…
Two-sided Competing Matching Recommendation Markets With Quota and Complementary Preferences Constraints
Yuantong Li, Guang Cheng, Xiaowu Dai
In this paper, we propose a new recommendation algorithm for addressing the problem of two-sided online matching markets with complementary preferences and quota constraints, where…
Dynamic Matching Bandit For Two-Sided Online Markets
Yuantong Li, Chi-hua Wang, Guang Cheng +1
Two-sided online matching platforms are employed in various markets. However, agents' preferences in the current market are usually implicit and unknown, thus needing to be learned…