most citedA General Neural Causal Model for Interactive Recommendation

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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…