7 citations · 17 across the 9 of their papers we have counts for
9 papers
Learning from a Learning User for Optimal Recommendations
Fan Yao, Chuanhao Li, Denis Nekipelov +2
In real-world recommendation problems, especially those with a formidably large item space, users have to gradually learn to estimate the utility of any fresh recommendations from…
IMO: Interactive Multi-Objective Off-Policy Optimization
Nan Wang, Hongning Wang, Maryam Karimzadehgan +2
Most real-world optimization problems have multiple objectives. A system designer needs to find a policy that trades off these objectives to reach a desired operating point. This p…
Learning Neural Ranking Models Online from Implicit User Feedback
Yiling Jia, Hongning Wang
Existing online learning to rank (OL2R) solutions are limited to linear models, which are incompetent to capture possible non-linear relations between queries and documents. In thi…
Calibrating Explore-Exploit Trade-off for Fair Online Learning to Rank
Yiling Jia, Hongning Wang
Online learning to rank (OL2R) has attracted great research interests in recent years, thanks to its advantages in avoiding expensive relevance labeling as required in offline supe…
Learning the Optimal Recommendation from Explorative Users
Fan Yao, Chuanhao Li, Denis Nekipelov +2
We propose a new problem setting to study the sequential interactions between a recommender system and a user. Instead of assuming the user is omniscient, static, and explicit, as…
Asynchronous Upper Confidence Bound Algorithms for Federated Linear Bandits
Chuanhao Li, Hongning Wang
Linear contextual bandit is a popular online learning problem. It has been mostly studied in centralized learning settings. With the surging demand of large-scale decentralized mod…