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Exploring author gender in book rating and recommendation

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Abstract

Collaborative filtering algorithms find useful patterns in rating and consumption data and exploit these patterns to guide users to good items. Many of these patterns reflect important real-world phenomena driving interactions between the various users and items; other patterns may be irrelevant or reflect undesired discrimination, such as discrimination in publishing or purchasing against authors who are women or ethnic minorities. In this work, we examine the response of collaborative filtering recommender algorithms to the distribution of their input data with respect to one dimension of social concern, namely content creator gender. Using publicly available book ratings data, we measure the distribution of the genders of the authors of books in user rating profiles and recommendation lists produced from this data. We find that common collaborative filtering algorithms tend to propagate at least some of each user’s tendency to rate or read male or female authors into their resulting recommendations, although they differ in both the strength of this propagation and the variance in the gender balance of the recommendation lists they produce. The data, experimental design, and statistical methods are designed to be reusable for studying potentially discriminatory social dimensions of recommendations in other domains and settings as well.

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Notes

  1. Documentation and code available at https://bookdata.piret.info.

  2. https://openlibrary.org/developers/dumps.

  3. https://www.loc.gov/cds/products/marcDist.php.

  4. http://viaf.org/viaf/data/.

  5. https://md.ekstrandom.net/pubs/bag-extended.

  6. To reduce the number of zeros, we tuned GoodReads using 1000-item lists instead of 100.

  7. In early iterations of this work, we used broader priors; these vague priors are more in line with current STAN recommendations (see https://github.com/stan-dev/stan/wiki/Prior-Choice-Recommendations), and do not affect inference conclusions.

  8. This does not accommodate authors with non-binary gender identities. Our goal here is examine the behavior of simple mechanisms supported by available data.

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Acknowledgements

We thank Mucun Tian, Mohammed R. Imran Kazi, and Hoda Mehrpouyan for their contributions to the conference paper on which this work builds, and the People and Information Research Team (PIReT) for their support and feedback to help refine this research agenda. Computation performed on the R2 cluster (Boise State Research Computing Department 2017).

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  1. People and Information Research Team, Department of Computer Science, Boise State University, Boise, USA

    Michael D. Ekstrand

  2. Department of Computer Science, University of Minnesota, Minneapolis, USA

    Daniel Kluver

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  1. Michael D. Ekstrand
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  2. Daniel Kluver
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Correspondence to Michael D. Ekstrand.

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This work was partially supported by the National Science Foundation under Grant IIS 17-51278. Full code to reproduce this paper’s experiments is available at https://md.ekstrandom.net/pubs/bag-extended.

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Ekstrand, M.D., Kluver, D. Exploring author gender in book rating and recommendation. User Model User-Adap Inter 31, 377–420 (2021). https://doi.org/10.1007/s11257-020-09284-2

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