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PRINCE: Provider-side Interpretability with Counterfactual Explanations
  in Recommender Systems

PRINCE: Provider-side Interpretability with Counterfactual Explanations in Recommender Systems

19 November 2019
Azin Ghazimatin
Oana Balalau
Rishiraj Saha Roy
G. Weikum
    FAtt
ArXivPDFHTML

Papers citing "PRINCE: Provider-side Interpretability with Counterfactual Explanations in Recommender Systems"

11 / 11 papers shown
Title
Towards Unifying Evaluation of Counterfactual Explanations: Leveraging Large Language Models for Human-Centric Assessments
Towards Unifying Evaluation of Counterfactual Explanations: Leveraging Large Language Models for Human-Centric Assessments
M. Domnich
Julius Valja
Rasmus Moorits Veski
Giacomo Magnifico
Kadi Tulver
Eduard Barbu
Raul Vicente
LRM
ELM
40
2
0
28 Oct 2024
RecRec: Algorithmic Recourse for Recommender Systems
RecRec: Algorithmic Recourse for Recommender Systems
Sahil Verma
Ashudeep Singh
Varich Boonsanong
John P. Dickerson
Chirag Shah
25
1
0
28 Aug 2023
Filter Bubbles in Recommender Systems: Fact or Fallacy -- A Systematic
  Review
Filter Bubbles in Recommender Systems: Fact or Fallacy -- A Systematic Review
Q. Areeb
Mohammad Nadeem
S. Sohail
Raza Imam
F. Doctor
Yassine Himeur
Amir Hussain
Abbes Amira
24
29
0
02 Jul 2023
Explainable Recommender with Geometric Information Bottleneck
Explainable Recommender with Geometric Information Bottleneck
Hanqi Yan
Lin Gui
Menghan Wang
Kun Zhang
Yulan He
13
2
0
09 May 2023
Explaining Recommendation System Using Counterfactual Textual
  Explanations
Explaining Recommendation System Using Counterfactual Textual Explanations
Niloofar Ranjbar
S. Momtazi
MohammadMehdi Homayounpour
27
4
0
14 Mar 2023
ExplainableFold: Understanding AlphaFold Prediction with Explainable AI
ExplainableFold: Understanding AlphaFold Prediction with Explainable AI
Juntao Tan
Yongfeng Zhang
17
6
0
27 Jan 2023
Learning to Counterfactually Explain Recommendations
Learning to Counterfactually Explain Recommendations
Yuanshun Yao
Chong Wang
Hang Li
CML
OffRL
22
1
0
17 Nov 2022
From Intrinsic to Counterfactual: On the Explainability of
  Contextualized Recommender Systems
From Intrinsic to Counterfactual: On the Explainability of Contextualized Recommender Systems
Yao Zhou
Haonan Wang
Jingrui He
Haixun Wang
24
15
0
28 Oct 2021
Improving Visualization Interpretation Using Counterfactuals
Improving Visualization Interpretation Using Counterfactuals
Smiti Kaul
D. Borland
Nan Cao
David Gotz
CML
8
17
0
21 Jul 2021
Counterfactual Explanations for Neural Recommenders
Counterfactual Explanations for Neural Recommenders
Khanh Tran
Azin Ghazimatin
Rishiraj Saha Roy
AAML
CML
52
65
0
11 May 2021
Counterfactuals and Causability in Explainable Artificial Intelligence:
  Theory, Algorithms, and Applications
Counterfactuals and Causability in Explainable Artificial Intelligence: Theory, Algorithms, and Applications
Yu-Liang Chou
Catarina Moreira
P. Bruza
Chun Ouyang
Joaquim A. Jorge
CML
42
176
0
07 Mar 2021
1