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International Workshop on Algorithmic Bias in Search and Recommendation

BIAS 2022: Advances in Bias and Fairness in Information Retrieval pp 35–42Cite as

Towards an Approach for Analyzing Dynamic Aspects of Bias and Beyond-Accuracy Measures

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Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1610))

Abstract

The quality of recommender systems has traditionally only been assessed using accuracy measures. Research has shown that accuracy is only one side of the medallion and that we should also consider quality features that go beyond accuracy. Recently, also fairness-related aspects and bias have increasingly been considered as outcome dimensions in this context. While beyond-accuracy measures including diversity, novelty and serendipity and bias in recommendation have been subject to the research discourse, their interrelation and temporal and group dynamics are clearly under-explored. In this position paper, we propose an approach that groups users based on their behaviors and preferences and that addresses beyond-accuracy needs of those groups while controlling for bias. Further, we consider the analysis of long-term dynamics of different interrelated beyond-accuracy measures and bias as crucial research direction since it helps to advance the field and to address societal issues related to recommender systems and personalization.

Supported by the Christian Doppler Research Association (CDG).

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Author information

Authors and Affiliations

  1. Christian Doppler Laboratory for Advancing the State-of-the-Art of Recommender Systems in Multi-domain Settings, Research Unit E-Commerce, TU Wien, Wien, Austria

    Julia Neidhardt & Mete Sertkan

Authors
  1. Julia Neidhardt
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  2. Mete Sertkan
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Corresponding author

Correspondence to Julia Neidhardt .

Editor information

Editors and Affiliations

  1. University of Cagliari, Cagliari, Italy

    Ludovico Boratto

  2. Sapienza University of Rome, Rome, Italy

    Stefano Faralli

  3. University of Cagliari, Cagliari, Italy

    Mirko Marras

  4. University of L’Aquila, L’Aquila, Italy

    Giovanni Stilo

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Neidhardt, J., Sertkan, M. (2022). Towards an Approach for Analyzing Dynamic Aspects of Bias and Beyond-Accuracy Measures. In: Boratto, L., Faralli, S., Marras, M., Stilo, G. (eds) Advances in Bias and Fairness in Information Retrieval. BIAS 2022. Communications in Computer and Information Science, vol 1610. Springer, Cham. https://doi.org/10.1007/978-3-031-09316-6_4

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  • DOI: https://doi.org/10.1007/978-3-031-09316-6_4

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-09315-9

  • Online ISBN: 978-3-031-09316-6

  • eBook Packages: Computer ScienceComputer Science (R0)

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