211 citations · 527 across the 28 of their papers we have counts for
9 papers · 1 filter
Debiasing Recommendation by Learning Identifiable Latent Confounders
Qing Zhang, Xiaoying Zhang, Yang Liu +4
Recommendation systems aim to predict users' feedback on items not exposed to them. Confounding bias arises due to the presence of unmeasured variables (e.g., the socio-economic st…
PairRank: Online Pairwise Learning to Rank by Divide-and-Conquer
Yiling Jia, Huazheng Wang, Stephen Guo +1
Online Learning to Rank (OL2R) eliminates the need of explicit relevance annotation by directly optimizing the rankers from their interactions with users. However, the required exp…
Reversible Action Design for Combinatorial Optimization with Reinforcement Learning
Fan Yao, Renqin Cai, Hongning Wang
Combinatorial optimization problem (COP) over graphs is a fundamental challenge in optimization. Reinforcement learning (RL) has recently emerged as a new framework to tackle these…
Learning from Crowds by Modeling Common Confusions
Zhendong Chu, Jing Ma, Hongning Wang
Crowdsourcing provides a practical way to obtain large amounts of labeled data at a low cost. However, the annotation quality of annotators varies considerably, which imposes new c…
Unifying Clustered and Non-stationary Bandits
Chuanhao Li, Qingyun Wu, Hongning Wang
Non-stationary bandits and online clustering of bandits lift the restrictive assumptions in contextual bandits and provide solutions to many important real-world scenarios. Though…
Model-Based Reinforcement Learning with Adversarial Training for Online Recommendation
Xueying Bai, Jian Guan, Hongning Wang
Reinforcement learning is well suited for optimizing policies of recommender systems. Current solutions mostly focus on model-free approaches, which require frequent interactions w…