Counteracting Bias and Increasing Fairness in Search and Recommender Systems

Gao, Ruoyuan; Shah, Chirag

doi:10.1145/3383313.3411545

Cited by 15 publications

(6 citation statements)

References 14 publications

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“…We consider an alternative approach based on sequential pattern mining (SPM) (Gan et al 2019). A pattern is a subsequence that occurs in at least one sequence maintaining the original item ordering.…”

Section: Semi-structured Sequential Datamentioning

confidence: 99%

Building Higher-Order Abstractions from the Components of Recommender Systems

Kadıoğlu,

Kleynhans

2024

AAAI

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We present a modular recommender system framework that tightly integrates yet maintains the independence of individual components, thus satisfying two of the most critical aspects of industrial applications, generality and specificity. On the one hand, we ensure that each component remains self-contained and is ready to serve in other applications beyond recommender systems. On the other hand, when these components are combined, a unified theme emerges for recommender systems. We present the details of each component in the context of recommender systems and other applications. We release each component as an open-source library, and most importantly, we release their integration under MAB2REC, an industry-strength open-source software for building bandit-based recommender systems. By bringing standalone components together, Mab2Rec realizes a powerful and scalable toolchain to build and deploy business-relevant personalization applications. Finally, we share our experience and best practices for user training, adoption, performance evaluation, deployment, and model governance within the enterprise and the broader community.

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Section: Semi-structured Sequential Datamentioning

confidence: 99%

Building Higher-Order Abstractions from the Components of Recommender Systems

Kadıoğlu,

Kleynhans

2024

AAAI

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“…This includes work by information science faculty like Bar-Ilan (2007a;2007b), Furner (2007, Saracevic (1999), Smith (1981), Spink et al (2001), andZimmer (2008). Fairness in information retrieval is the central focus of Noble's book, a topic discussed by many other information science researchers beyond those previously cited, including Shah (Gao & Shah, 2020a, 2020b and Jansen (Eastman & Jansen, 2003;Jansen & Schuster, 2011).…”

Section: What Contribution Has Information Science Provided To Our Un...mentioning

confidence: 99%

What Does Information Science Offer for Data Science Research?: A Review of Data and Information Ethics Literature

Lund

Wang

2022

Journal of Data and Information Science

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This paper reviews literature pertaining to the development of data science as a discipline, current issues with data bias and ethics, and the role that the discipline of information science may play in addressing these concerns. Information science research and researchers have much to offer for data science, owing to their background as transdisciplinary scholars who apply human-centered and social-behavioral perspectives to issues within natural science disciplines. Information science researchers have already contributed to a humanistic approach to data ethics within the literature and an emphasis on data science within information schools all but ensures that this literature will continue to grow in coming decades. This review article serves as a reference for the history, current progress, and potential future directions of data ethics research within the corpus of information science literature.

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“…Significant progress has been made in the space of algorithmic fairness [56,67] for recommender systems [17,25,49], as reflected by the FAccTRec workshop series since it first took place in 2017 [18,19,38].…”

Section: The Evolution Of Recommendation Fairnessmentioning

confidence: 99%

Recommendation Fairness: From Static to Dynamic

Zhang¹,

Wang²

2021

Preprint

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Driven by the need to capture users' evolving interests and optimize their long-term experiences, more and more recommender systems have started to model recommendation as a Markov decision process and employ reinforcement learning to address the problem. Shouldn't research on the fairness of recommender systems follow the same trend from static evaluation and one-shot intervention to dynamic monitoring and non-stop control? In this paper, we portray the recent developments in recommender systems first and then discuss how fairness could be baked into the reinforcement learning techniques for recommendation. Moreover, we argue that in order to make further progress in recommendation fairness, we may want to consider multi-agent (game-theoretic) optimization, multi-objective (Pareto) optimization, and simulationbased optimization, in the general framework of stochastic games. CCS CONCEPTS• Information systems → Recommender systems; • Computing methodologies → Reinforcement learning.

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Counteracting Bias and Increasing Fairness in Search and Recommender Systems

Cited by 15 publications

References 14 publications

Building Higher-Order Abstractions from the Components of Recommender Systems

Building Higher-Order Abstractions from the Components of Recommender Systems

What Does Information Science Offer for Data Science Research?: A Review of Data and Information Ethics Literature

Recommendation Fairness: From Static to Dynamic

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