Improving Recommendation Fairness via Data Augmentation

• URL: http://arxiv.org/abs/2302.06333v1
• Date: Mon, 13 Feb 2023 13:11:46 GMT
• Title: Improving Recommendation Fairness via Data Augmentation
• Authors: Lei Chen, Le Wu, Kun Zhang, Richang Hong, Defu Lian, Zhiqiang Zhang, Jun Zhou, Meng Wang
• Abstract summary: Collaborative filtering based recommendation learns users' preferences from all users' historical behavior data, and has been popular to facilitate decision making. A recommender system is considered unfair when it does not perform equally well for different user groups according to users' sensitive attributes. In this paper, we study how to improve recommendation fairness from the data augmentation perspective.
• Score: 66.4071365614835
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Collaborative filtering based recommendation learns users' preferences from all users' historical behavior data, and has been popular to facilitate decision making. R Recently, the fairness issue of recommendation has become more and more essential. A recommender system is considered unfair when it does not perform equally well for different user groups according to users' sensitive attributes~(e.g., gender, race). Plenty of methods have been proposed to alleviate unfairness by optimizing a predefined fairness goal or changing the distribution of unbalanced training data. However, they either suffered from the specific fairness optimization metrics or relied on redesigning the current recommendation architecture. In this paper, we study how to improve recommendation fairness from the data augmentation perspective. The recommendation model amplifies the inherent unfairness of imbalanced training data. We augment imbalanced training data towards balanced data distribution to improve fairness. The proposed framework is generally applicable to any embedding-based recommendation, and does not need to pre-define a fairness metric. Extensive experiments on two real-world datasets clearly demonstrate the superiority of our proposed framework. We publish the source code at https://github.com/newlei/FDA.

Related papers

• FairDgcl: Fairness-aware Recommendation with Dynamic Graph Contrastive Learning [48.38344934125999]
  We study how to implement high-quality data augmentation to improve recommendation fairness. Specifically, we propose FairDgcl, a dynamic graph adversarial contrastive learning framework. We show that FairDgcl can simultaneously generate enhanced representations that possess both fairness and accuracy.
  arXiv  Detail & Related papers  (2024-10-23T04:43:03Z)
• Preference Diffusion for Recommendation [50.8692409346126]
  We propose PreferDiff, a tailored optimization objective for DM-based recommenders. PreferDiff transforms BPR into a log-likelihood ranking objective to better capture user preferences. It is the first personalized ranking loss designed specifically for DM-based recommenders.
  arXiv  Detail & Related papers  (2024-10-17T01:02:04Z)
• Boosting Fair Classifier Generalization through Adaptive Priority Reweighing [59.801444556074394]
  A performance-promising fair algorithm with better generalizability is needed. This paper proposes a novel adaptive reweighing method to eliminate the impact of the distribution shifts between training and test data on model generalizability.
  arXiv  Detail & Related papers  (2023-09-15T13:04:55Z)
• Equal Experience in Recommender Systems [21.298427869586686]
  We introduce a novel fairness notion (that we call equal experience) to regulate unfairness in the presence of biased data. We propose an optimization framework that incorporates the fairness notion as a regularization term, as well as introduce computationally-efficient algorithms that solve the optimization.
  arXiv  Detail & Related papers  (2022-10-12T05:53:05Z)
• Balancing Accuracy and Fairness for Interactive Recommendation with Reinforcement Learning [68.25805655688876]
  Fairness in recommendation has attracted increasing attention due to bias and discrimination possibly caused by traditional recommenders. We propose a reinforcement learning based framework, FairRec, to dynamically maintain a long-term balance between accuracy and fairness in IRS. Extensive experiments validate that FairRec can improve fairness, while preserving good recommendation quality.
  arXiv  Detail & Related papers  (2021-06-25T02:02:51Z)
• DeepFair: Deep Learning for Improving Fairness in Recommender Systems [63.732639864601914]
  The lack of bias management in Recommender Systems leads to minority groups receiving unfair recommendations. We propose a Deep Learning based Collaborative Filtering algorithm that provides recommendations with an optimum balance between fairness and accuracy without knowing demographic information about the users.
  arXiv  Detail & Related papers  (2020-06-09T13:39:38Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.