User-oriented Fairness in Recommendation

• URL: http://arxiv.org/abs/2104.10671v1
• Date: Wed, 21 Apr 2021 17:50:31 GMT
• Title: User-oriented Fairness in Recommendation
• Authors: Yunqi Li, Hanxiong Chen, Zuohui Fu, Yingqiang Ge, Yongfeng Zhang
• Abstract summary: We address the unfairness problem in recommender systems from the user perspective. We group users into advantaged and disadvantaged groups according to their level of activity. Our approach can not only improve group fairness of users in recommender systems, but also achieve better overall recommendation performance.
• Score: 21.651482297198687
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: As a highly data-driven application, recommender systems could be affected by data bias, resulting in unfair results for different data groups, which could be a reason that affects the system performance. Therefore, it is important to identify and solve the unfairness issues in recommendation scenarios. In this paper, we address the unfairness problem in recommender systems from the user perspective. We group users into advantaged and disadvantaged groups according to their level of activity, and conduct experiments to show that current recommender systems will behave unfairly between two groups of users. Specifically, the advantaged users (active) who only account for a small proportion in data enjoy much higher recommendation quality than those disadvantaged users (inactive). Such bias can also affect the overall performance since the disadvantaged users are the majority. To solve this problem, we provide a re-ranking approach to mitigate this unfairness problem by adding constraints over evaluation metrics. The experiments we conducted on several real-world datasets with various recommendation algorithms show that our approach can not only improve group fairness of users in recommender systems, but also achieve better overall recommendation performance.

Related papers

• Correcting for Popularity Bias in Recommender Systems via Item Loss Equalization [1.7771454131646311]
  A small set of popular items dominate the recommendation results due to their high interaction rates. This phenomenon disproportionately benefits users with mainstream tastes while neglecting those with niche interests. We propose an in-processing approach to address this issue by intervening in the training process of recommendation models.
  arXiv  Detail & Related papers  (2024-10-07T08:34:18Z)
• A Survey on Fairness-aware Recommender Systems [59.23208133653637]
  We present concepts of fairness in different recommendation scenarios, comprehensively categorize current advances, and introduce typical methods to promote fairness in different stages of recommender systems. Next, we delve into the significant influence that fairness-aware recommender systems exert on real-world industrial applications.
  arXiv  Detail & Related papers  (2023-06-01T07:08:22Z)
• Improving Recommendation Fairness via Data Augmentation [66.4071365614835]
  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.
  arXiv  Detail & Related papers  (2023-02-13T13:11:46Z)
• 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)
• Breaking Feedback Loops in Recommender Systems with Causal Inference [99.22185950608838]
  Recent work has shown that feedback loops may compromise recommendation quality and homogenize user behavior. We propose the Causal Adjustment for Feedback Loops (CAFL), an algorithm that provably breaks feedback loops using causal inference. We show that CAFL improves recommendation quality when compared to prior correction methods.
  arXiv  Detail & Related papers  (2022-07-04T17:58:39Z)
• Experiments on Generalizability of User-Oriented Fairness in Recommender Systems [2.0932879442844476]
  A fairness-aware recommender system aims to treat different user groups similarly. We propose a user-centered fairness re-ranking framework applied on top of a base ranking model. We evaluate the final recommendations provided by the re-ranking framework from both user- (e.g., NDCG) and item-side (e.g., novelty, item-fairness) metrics.
  arXiv  Detail & Related papers  (2022-05-17T12:36:30Z)
• Joint Multisided Exposure Fairness for Recommendation [76.75990595228666]
  This paper formalizes a family of exposure fairness metrics that model the problem jointly from the perspective of both the consumers and producers. Specifically, we consider group attributes for both types of stakeholders to identify and mitigate fairness concerns that go beyond individual users and items towards more systemic biases in recommendation.
  arXiv  Detail & Related papers  (2022-04-29T19:13:23Z)
• The Unfairness of Active Users and Popularity Bias in Point-of-Interest Recommendation [4.578469978594752]
  This paper studies the interplay between (i) the unfairness of active users, (ii) the unfairness of popular items, and (iii) the accuracy of recommendation as three angles of our study triangle. For item fairness, we divide items into short-head, mid-tail, and long-tail groups and study the exposure of these item groups into the top-k recommendation list of users. Our study shows that most recommendation models cannot satisfy both consumer and producer fairness, indicating a trade-off between these variables possibly due to natural biases in data.
  arXiv  Detail & Related papers  (2022-02-27T08:02:19Z)
• 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)
• Fairness-Aware Explainable Recommendation over Knowledge Graphs [73.81994676695346]
  We analyze different groups of users according to their level of activity, and find that bias exists in recommendation performance between different groups. We show that inactive users may be more susceptible to receiving unsatisfactory recommendations, due to insufficient training data for the inactive users. We propose a fairness constrained approach via re-ranking to mitigate this problem in the context of explainable recommendation over knowledge graphs.
  arXiv  Detail & Related papers  (2020-06-03T05:04:38Z)

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.