18 citations · 24 across the 6 of their papers we have counts for
6 papers
Meta-Reinforcement Learning via Exploratory Task Clustering
Zhendong Chu, Hongning Wang
Meta-reinforcement learning (meta-RL) aims to quickly solve new tasks by leveraging knowledge from prior tasks. However, previous studies often assume a single mode homogeneous tas…
Graph-based Extractive Explainer for Recommendations
Peng Wang, Renqin Cai, Hongning Wang
Explanations in a recommender system assist users in making informed decisions among a set of recommended items. Great research attention has been devoted to generating natural lan…
Comparative Explanations of Recommendations
Aobo Yang, Nan Wang, Renqin Cai +2
As recommendation is essentially a comparative (or ranking) process, a good explanation should illustrate to users why an item is believed to be better than another, i.e., comparat…
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 to Structure Long-term Dependence for Sequential Recommendation
Renqin Cai, Qinglei Wang, Chong Wang +1
Sequential recommendation recommends items based on sequences of users' historical actions. The key challenge in it is how to effectively model the influence from distant actions t…
Déjà vu: A Contextualized Temporal Attention Mechanism for Sequential Recommendation
Jibang Wu, Renqin Cai, Hongning Wang
Predicting users' preferences based on their sequential behaviors in history is challenging and crucial for modern recommender systems. Most existing sequential recommendation algo…