From Intrinsic to Counterfactual: On the Explainability of Contextualized Recommender Systems

28 October 2021

Papers citing "From Intrinsic to Counterfactual: On the Explainability of Contextualized Recommender Systems"

10 / 10 papers shown

Title
Stability of Explainable Recommendation Sairamvinay Vijayaraghavan Prasant Mohapatra AAML 33 1 0 03 May 2024
Mitigating Pooling Bias in E-commerce Search via False Negative Estimation Xiaochen Wang Xiao Xiao Ruhan Zhang Xuan Zhang Taesik Na Tejaswi Tenneti Haixun Wang Fenglong Ma 13 0 0 11 Nov 2023
Explaining Recommendation System Using Counterfactual Textual Explanations Niloofar Ranjbar S. Momtazi MohammadMehdi Homayounpour 19 3 0 14 Mar 2023
Non-IID Transfer Learning on Graphs Jun Wu Jingrui He Elizabeth Ainsworth OOD 4 33 0 15 Dec 2022
Neural Bandit with Arm Group Graph Yunzhe Qi Yikun Ban Jingrui He 12 10 0 08 Jun 2022
DISCO: Comprehensive and Explainable Disinformation Detection Dongqi Fu Yikun Ban Hanghang Tong Ross Maciejewski Jingrui He 24 22 0 09 Mar 2022
Sequential Recommendation via Stochastic Self-Attention Ziwei Fan Zhiwei Liu Alice Wang Zahra Nazari Lei Zheng Hao Peng Philip S. Yu AI4TS 11 138 0 16 Jan 2022
Federated Social Recommendation with Graph Neural Network Zhiwei Liu Liangwei Yang Ziwei Fan Hao Peng Philip S. Yu FedML 21 151 0 21 Nov 2021
Benchmarking Automated Clinical Language Simplification: Dataset, Algorithm, and Evaluation Junyu Luo Zifei Zheng Hanzhong Ye Muchao Ye Yaqing Wang Quanzeng You Cao Xiao Fenglong Ma 14 5 0 04 Dec 2020
Counterfactual Explanations and Algorithmic Recourses for Machine Learning: A Review Sahil Verma Varich Boonsanong Minh Hoang Keegan E. Hines John P. Dickerson Chirag Shah CML 24 106 0 20 Oct 2020