1 citations · 1 across the 2 of their papers we have counts for
2 papers
cs.LG2023
Adversarial Batch Inverse Reinforcement Learning: Learn to Reward from Imperfect Demonstration for Interactive Recommendation
Jialin Liu, Xinyan Su, Zeyu He +2
Rewards serve as a measure of user satisfaction and act as a limiting factor in interactive recommender systems. In this research, we focus on the problem of learning to reward (LT…
cs.LG2023★ 1 cited
A General Neural Causal Model for Interactive Recommendation
Jialin Liu, Xinyan Su, Peng Zhou +2
Survivor bias in observational data leads the optimization of recommender systems towards local optima. Currently most solutions re-mines existing human-system collaboration patter…