Consumer-side Fairness in Recommender Systems: A Systematic Survey of Methods and Evaluation

• URL: http://arxiv.org/abs/2305.09330v1
• Date: Tue, 16 May 2023 10:07:41 GMT
• Title: Consumer-side Fairness in Recommender Systems: A Systematic Survey of Methods and Evaluation
• Authors: Bj{\o}rnar Vass{\o}y and Helge Langseth
• Abstract summary: Growing awareness of discrimination in machine learning methods motivated both academia and industry to research how fairness can be ensured in recommender systems. For recommender systems, such issues are well exemplified by occupation recommendation, where biases in historical data may lead to recommender systems relating one gender to lower wages or to the propagation of stereotypes. This survey serves as a systematic overview and discussion of the current research on consumer-side fairness in recommender systems.
• Score: 1.4123323039043334
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In the current landscape of ever-increasing levels of digitalization, we are facing major challenges pertaining to scalability. Recommender systems have become irreplaceable both for helping users navigate the increasing amounts of data and, conversely, aiding providers in marketing products to interested users. The growing awareness of discrimination in machine learning methods has recently motivated both academia and industry to research how fairness can be ensured in recommender systems. For recommender systems, such issues are well exemplified by occupation recommendation, where biases in historical data may lead to recommender systems relating one gender to lower wages or to the propagation of stereotypes. In particular, consumer-side fairness, which focuses on mitigating discrimination experienced by users of recommender systems, has seen a vast number of diverse approaches for addressing different types of discrimination. The nature of said discrimination depends on the setting and the applied fairness interpretation, of which there are many variations. This survey serves as a systematic overview and discussion of the current research on consumer-side fairness in recommender systems. To that end, a novel taxonomy based on high-level fairness interpretation is proposed and used to categorize the research and their proposed fairness evaluation metrics. Finally, we highlight some suggestions for the future direction of the field.

Related papers

• Metrics for popularity bias in dynamic recommender systems [0.0]
  Biased recommendations may lead to decisions that can potentially have adverse effects on individuals, sensitive user groups, and society. This paper focuses on quantifying popularity bias that stems directly from the output of RecSys models. Four metrics to quantify popularity bias in RescSys over time in dynamic setting across different sensitive user groups have been proposed.
  arXiv  Detail & Related papers  (2023-10-12T16:15:30Z)
• Impression-Aware Recommender Systems [57.38537491535016]
  Novel data sources bring new opportunities to improve the quality of recommender systems. Researchers may use impressions to refine user preferences and overcome the current limitations in recommender systems research. We present a systematic literature review on recommender systems using impressions.
  arXiv  Detail & Related papers  (2023-08-15T16:16:02Z)
• 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)
• Fairness in Recommendation: Foundations, Methods and Applications [38.63520487389138]
  This survey focuses on the foundations for fairness in recommendation literature. It first presents a brief introduction about fairness in basic machine learning tasks such as classification and ranking. After that, the survey will introduce fairness in recommendation with a focus on the definitions, the typical techniques for improving fairness, as well as the datasets for fairness studies in recommendation.
  arXiv  Detail & Related papers  (2022-05-26T20:48:53Z)
• Fairness in Recommender Systems: Research Landscape and Future Directions [119.67643184567623]
  We review the concepts and notions of fairness that were put forward in the area in the recent past. We present an overview of how research in this field is currently operationalized. Overall, our analysis of recent works points to certain research gaps.
  arXiv  Detail & Related papers  (2022-05-23T08:34:25Z)
• 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)
• CPFair: Personalized Consumer and Producer Fairness Re-ranking for Recommender Systems [5.145741425164946]
  We present an optimization-based re-ranking approach that seamlessly integrates fairness constraints from both the consumer and producer-side. We demonstrate through large-scale experiments on 8 datasets that our proposed method is capable of improving both consumer and producer fairness without reducing overall recommendation quality.
  arXiv  Detail & Related papers  (2022-04-17T20:38:02Z)
• Through the Data Management Lens: Experimental Analysis and Evaluation of Fair Classification [75.49600684537117]
  Data management research is showing an increasing presence and interest in topics related to data and algorithmic fairness. We contribute a broad analysis of 13 fair classification approaches and additional variants, over their correctness, fairness, efficiency, scalability, and stability. Our analysis highlights novel insights on the impact of different metrics and high-level approach characteristics on different aspects of performance.
  arXiv  Detail & Related papers  (2021-01-18T22:55:40Z)
• 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.