ResearchTrend.AI
  • Papers
  • Communities
  • Events
  • Blog
  • Pricing
Papers
Communities
Social Events
Terms and Conditions
Pricing
Parameter LabParameter LabTwitterGitHubLinkedInBlueskyYoutube

© 2025 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"

8 / 8 papers shown
Title
Unmasking Gender Bias in Recommendation Systems and Enhancing Category-Aware Fairness
Unmasking Gender Bias in Recommendation Systems and Enhancing Category-Aware Fairness
Tahsin Alamgir Kheya
Mohamed Reda Bouadjenek
Sunil Aryal
61
0
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 Systems
Yongsu Ahn
Yu-Ru Lin
CML
59
3
0
29 Dec 2023
Towards Individual and Multistakeholder Fairness in Tourism Recommender
  Systems
Towards Individual and Multistakeholder Fairness in Tourism Recommender Systems
Ashmi Banerjee
Paromita Banik
Wolfgang Wörndl
59
14
0
05 Sep 2023
FairRoad: Achieving Fairness for Recommender Systems with Optimized
  Antidote Data
FairRoad: Achieving Fairness for Recommender Systems with Optimized Antidote Data
Minghong Fang
Jia-Wei Liu
Michinari Momma
Yi Sun
60
4
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
119
90
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
10
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
53
45
0
09 Jun 2020
Flatter is better: Percentile Transformations for Recommender Systems
Flatter is better: Percentile Transformations for Recommender Systems
M. Mansoury
Robin Burke
B. Mobasher
18
5
0
10 Jul 2019
1