ResearchTrend.AI
  • Communities
  • Connect sessions
  • AI calendar
  • Organizations
  • Join Slack
  • Contact Sales
Papers
Communities
Social Events
Terms and Conditions
Pricing
Contact Sales
Parameter LabParameter LabTwitterGitHubLinkedInBlueskyYoutube

© 2026 ResearchTrend.AI, All rights reserved.

  1. Home
  2. Papers
  3. 1908.00831
  4. Cited By
Bias Disparity in Collaborative Recommendation: Algorithmic Evaluation
  and Comparison

Bias Disparity in Collaborative Recommendation: Algorithmic Evaluation and Comparison

2 August 2019
M. Mansoury
B. Mobasher
Robin Burke
Mykola Pechenizkiy
    FaML
ArXiv (abs)PDFHTML

Papers citing "Bias Disparity in Collaborative Recommendation: Algorithmic Evaluation and Comparison"

9 / 9 papers shown
The Urban Impact of AI: Modeling Feedback Loops in Next-Venue Recommendation
The Urban Impact of AI: Modeling Feedback Loops in Next-Venue Recommendation
G. Mauro
Marco Minici
Luca Pappalardo
HAI
205
4
0
10 Apr 2025
Unmasking Gender Bias in Recommendation Systems and Enhancing Category-Aware Fairness
Unmasking Gender Bias in Recommendation Systems and Enhancing Category-Aware FairnessThe Web Conference (WWW), 2025
Tahsin Alamgir Kheya
Mohamed Reda Bouadjenek
Sunil Aryal
301
6
0
25 Feb 2025
Break Out of a Pigeonhole: A Unified Framework for Examining
  Miscalibration, Bias, and Stereotype in Recommender Systems
Break Out of a Pigeonhole: A Unified Framework for Examining Miscalibration, Bias, and Stereotype in Recommender SystemsACM Transactions on Intelligent Systems and Technology (ACM TIST), 2023
Yongsu Ahn
Yu-Ru Lin
CML
260
6
0
29 Dec 2023
Towards Individual and Multistakeholder Fairness in Tourism Recommender
  Systems
Towards Individual and Multistakeholder Fairness in Tourism Recommender SystemsFrontiers in Big Data (FBD), 2023
Ashmi Banerjee
Paromita Banik
Wolfgang Wörndl
279
28
0
05 Sep 2023
FairRoad: Achieving Fairness for Recommender Systems with Optimized
  Antidote Data
FairRoad: Achieving Fairness for Recommender Systems with Optimized Antidote DataACM Symposium on Access Control Models and Technologies (SACMAT), 2022
Minghong Fang
Jia-Wei Liu
Michinari Momma
Yi Sun
215
5
0
13 Dec 2022
Fairness in Recommender Systems: Research Landscape and Future
  Directions
Fairness in Recommender Systems: Research Landscape and Future Directions
Yashar Deldjoo
Dietmar Jannach
Alejandro Bellogín
Alessandro Difonzo
Dario Zanzonelli
OffRLFaML
483
149
0
23 May 2022
How to Put Users in Control of their Data in Federated Top-N
  Recommendation with Learning to Rank
How to Put Users in Control of their Data in Federated Top-N Recommendation with Learning to Rank
Vito Walter Anelli
Yashar Deldjoo
Tommaso Di Noia
Antonio Ferrara
Fedelucio Narducci
FedML
178
1
0
17 Aug 2020
DeepFair: Deep Learning for Improving Fairness in Recommender Systems
DeepFair: Deep Learning for Improving Fairness in Recommender Systems
Jesús Bobadilla
R. Lara-Cabrera
Ángel González-Prieto
Fernando Ortega
FaML
153
51
0
09 Jun 2020
Flatter is better: Percentile Transformations for Recommender Systems
Flatter is better: Percentile Transformations for Recommender SystemsACM Transactions on Intelligent Systems and Technology (ACM TIST), 2019
M. Mansoury
Robin Burke
B. Mobasher
133
6
0
10 Jul 2019
1
Page 1 of 1