Skip to main content
SpringerLink
Log in

Fairness in Recommender Systems

  • Chapter
  • First Online:
Recommender Systems Handbook

Abstract

Recent research has added the idea of fairness to the suite of concerns beyond accuracy or user satisfaction that recommender systems researchers and practitioners consider in their work. Recommender systems pose unique challenges for investigating the fairness and non-discrimination concepts that have been developed in other machine learning literature. The multistakeholder nature of recommender applications, the ranked outputs, the centrality of personalization, and the role of user response complicate the problem of identifying precisely what types and operationalizations of fairness may be relevant. In this chapter, we lay out various ways a recommender system may be unfair and provide a conceptual framework for identifying the fairness that arise in an application and designing a project to assess and mitigate them. We then survey the literature to date on fair recommendation and provide pointers to other research on algorithmic fairness we believe is a promising basis for improving the fairness of recommender systems.

Portions of this chapter are adapted and condensed from: Michael D. Ekstrand, Anubrata Das, Robin Burke and Fernando Diaz (2021), “Fairness in Search and Recommendation”, Foundations and TrendsⓇ in Information Retrieval, Forthcoming. https://doi.org/10.1561/1500000079.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 259.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 329.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 329.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Notes

  1. 1.

    In the conference version of the author gender paper, Ekstrand et al. [28] described the goal of promoting equality of opportunity for book authors, but measured fairness through the proportional composition of ranked lists. Exposure-oriented metrics [8, 24] would be a more coherent way of advancing the stated goal. The journal version described here more clearly contextualizes the capabilities and implications of the methods employed, because we’re continually advancing our own understanding of these practices as well.

  2. 2.

    https://www.upf.edu/web/mtg/lastfm360k.

  3. 3.

    https://bookdata.piret.info.

  4. 4.

    http://www.ubereats.com/.

References

  1. H. Abdollahpouri, Popularity bias in recommendation: a multi-stakeholder perspective. PhD thesis, University of Colorado Boulder, 2020

    Google Scholar 

  2. M. Ali, P. Sapiezynski, M. Bogen, A. Korolova, A. Mislove, A. Rieke, Discrimination through optimization: how Facebook’s ad delivery can lead to biased outcomes, in Proceedings of the ACM on Human-Computer Interaction, vol. 3, no. CSCW (2019), pp. 1–30. https://doi.org/10.1145/3359301

  3. S. Barocas, A.D. Selbst, Big data’s disparate impact. Calif. Law Rev. 104(3), 671 (2016). https://doi.org/10.15779/Z38BG31

  4. S. Barocas, M. Hardt, A. Narayanan, Fairness and Machine Learning: Limitations and Opportunities (2019). https://fairmlbook.org/

  5. J. Beel, V. Brunel, Data pruning in recommender systems research: Best-Practice or malpractice? in ACM RecSys 2019 Late-Breaking Results (2019)

    Google Scholar 

  6. A. Beutel, J. Chen, Z. Zhao, E.H. Chi, Data decisions and theoretical implications when adversarially learning fair representations. Preprint (2017). https://doi.org/1707.00075

  7. A. Beutel, E.H. Chi, C. Goodrow, J. Chen, T. Doshi, H. Qian, L. Wei, Y. Wu, L. Heldt, Z. Zhao, L. Hong, Fairness in recommendation ranking through pairwise comparisons, in Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining (ACM, New York, 2019). https://doi.org/10.1145/3292500.3330745

    Google Scholar 

  8. A.J. Biega, K.P. Gummadi, G. Weikum, Equity of attention: amortizing individual fairness in rankings, in The 41st International ACM SIGIR Conference on Research & Development in Information Retrieval (ACM, New York, 2018), pp. 405–414. https://doi.org/10.1145/3209978.3210063

    Google Scholar 

  9. A.J. Biega, F. Diaz, M.D. Ekstrand, S. Kohlmeier, Overview of the TREC 2019 fair ranking track, in Proceedings of the Twenty-Eighth Text REtrieval Conference (TREC 2019) (2020)

    Google Scholar 

  10. A. Billey, M. Haugen, J. Hostage, N. Sack, A.L. Schiff, Report of the PCC ad hoc task group on gender in name authority records. Tech. rep., Program for Cooperative Cataloging (2016). https://www.loc.gov/aba/pcc/documents/Gender_375%20field_RecommendationReport.pdf

  11. J. Buolamwini, T. Gebru, Gender shades: intersectional accuracy disparities in commercial gender classification, in Proceedings of the 1st Conference on Fairness, Accountability, and Transparency, PMLR, vol. 81 (2018), pp. 77–91

    Google Scholar 

  12. R. Burke, Multisided fairness for recommendation. Preprint (2017). https://doi.org/1707.00093

  13. R. Burke, J. Kontny, N. Sonboli, Synthetic attribute data for evaluating consumer-side fairness. Preprint (2018). https://doi.org/1809.04199

  14. R. Burke, N. Sonboli, A. Ordonez-Gauger, Balanced neighborhoods for multi-sided fairness in recommendation, in Proceedings of the 1st Conference on Fairness, Accountability and Transparency, PMLR, vol. 81, ed. by S.A. Friedler, C. Wilson (2018), pp. 202–214

    Google Scholar 

  15. R. Cañamares, P. Castells, Should I follow the crowd?: a probabilistic analysis of the effectiveness of popularity in recommender systems, in The 41st International ACM SIGIR Conference on Research & Development in Information Retrieval (ACM, New York, 2018), pp. 415–424. https://doi.org/10.1145/3209978.3210014

    Google Scholar 

  16. J. Carbonell, J. Goldstein, The use of MMR, diversity-based reranking for reordering documents and producing summaries, in Proceedings of the 21st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval (ACM, New York, 1998), pp. 335–336. https://doi.org/10.1145/290941.291025

    Google Scholar 

  17. Ò. Celma, Music Recommendation and Discovery: The Long Tail, Long Fail, and Long Play in the Digital Music Space (Springer, Berlin, 2010). https://doi.org/10.1007/978-3-642-13287-2

    Book  Google Scholar 

  18. A.J.B. Chaney, B.M. Stewart, B.E. Engelhardt, How algorithmic confounding in recommendation systems increases homogeneity and decreases utility, in Proceedings of the 12th ACM Conference on Recommender Systems (ACM, New York, 2018), pp. 224–232. https://doi.org/10.1145/3240323.3240370

    Google Scholar 

  19. I. Chen, F.D. Johansson, D. Sontag, Why is my classifier discriminatory? in Advances in Neural Information Processing Systems, vol. 31, ed. by S. Bengio, H. Wallach, H. Larochelle, K. Grauman, N. Cesa-Bianchi, R. Garnett (2018), pp. 3539–3550

    Google Scholar 

  20. K. Crawford, The trouble with bias, in Neural Information Processing Systems (2017)

    Google Scholar 

  21. K. Crenshaw, Demarginalizing the intersection of race and sex: a black feminist critique of antidiscrimination doctrine, feminist theory and antiracist politics. Univ. Chic. Leg. Forum 1989, 139–168 (1989)

    Google Scholar 

  22. A. Das, M. Lease, A conceptual framework for evaluating fairness in search. Preprint (2019). https://doi.org/1907.09328

  23. Y. Deldjoo, V.W. Anelli, H. Zamani, A. Bellogin, T. Di Noia, Recommender systems fairness evaluation via generalized cross entropy, in Proceedings of the Workshop on Recommendation in Multi-stakeholder Environments at RecSys ’19, CEUR-WS, vol. 2440 (2019)

    Google Scholar 

  24. F. Diaz, B. Mitra, M.D. Ekstrand, A.J. Biega, B. Carterette, Evaluating stochastic rankings with expected exposure, in Proceedings of the 29th ACM International Conference on Information and Knowledge Management (ACM, New York, 2020). https://doi.org/10.1145/3340531.3411962

    Google Scholar 

  25. C. Dwork, M. Hardt, T. Pitassi, O. Reingold, R. Zemel, Fairness through awareness, in Proceedings of the 3rd Innovations in Theoretical Computer Science Conference (ACM, New York, 2012), pp. 214–226. https://doi.org/10.1145/2090236.2090255

    Book  MATH  Google Scholar 

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

  27. M.D. Ekstrand, M. Tian, I.M. Azpiazu, J.D. Ekstrand, O. Anuyah, D. McNeill, M.S. Pera, All the cool kids, how do they fit in?: Popularity and demographic biases in recommender evaluation and effectiveness, in Proceedings of the Conference on Fairness, Accountability, and Transparency (PMLR), New York, PMLR, vol. 81, ed. by S.A. Friedler, C. Wilson (2018), pp. 172–186

    Google Scholar 

  28. M.D. Ekstrand, M. Tian, M.R.I. Kazi, H. Mehrpouyan, D. Kluver, Exploring author gender in book rating and recommendation, in Proceedings of the Twelfth ACM Conference on Recommender Systems (ACM, New York, 2018). https://doi.org/10.1145/3240323.3240373

    Google Scholar 

  29. D. Ensign, S.A. Friedler, S. Neville, C. Scheidegger, S. Venkatasubramanian, Runaway feedback loops in predictive policing, in Proceedings of the 1st Conference on Fairness, Accountability and Transparency, New York, PMLR, vol. 81, ed. by S.A. Friedler, C. Wilson (2018), pp. 160–171

    Google Scholar 

  30. M. Feldman, S.A. Friedler, J. Moeller, C. Scheidegger, S. Venkatasubramanian, Certifying and removing disparate impact, in Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (ACM, New York, 2015), pp. 259–268. https://doi.org/10.1145/2783258.2783311

    Google Scholar 

  31. A. Ferraro, Music cold-start and long-tail recommendation: bias in deep representations, in Proceedings of the 13th ACM Conference on Recommender Systems (ACM, New York, 2019), pp. 586–590. https://doi.org/10.1145/3298689.3347052

    Google Scholar 

  32. B. Fish, A. Bashardoust, D. Boyd, S. Friedler, C. Scheidegger, S. Venkatasubramanian, Gaps in information access in social networks? in WWW ’19: The World Wide Web Conference (ACM, New York, 2019), pp. 480–490. https://doi.org/10.1145/3308558.3313680

    Google Scholar 

  33. T. Gebru, J. Morgenstern, B. Vecchione, J.W. Vaughan, H. Wallach, H. Daumeé III, K. Crawford, Datasheets for datasets. Preprint (2018). https://doi.org/1803.09010

  34. S.C. Geyik, S. Ambler, K. Kenthapadi, Fairness-Aware ranking in search & recommendation systems with application to LinkedIn talent search, in Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining (ACM, New York, 2019), pp. 2221–2231. https://doi.org/10.1145/3292500.3330691

    Google Scholar 

  35. F. Hamidi, M.K. Scheuerman, S.M. Branham, Gender recognition or gender reductionism?: The social implications of embedded gender recognition systems, in Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems (ACM, New York, 2018), p. 8. https://doi.org/10.1145/3173574.3173582

    Google Scholar 

  36. A. Hanna, E. Denton, A. Smart, J. Smith-Loud, Towards a critical race methodology in algorithmic fairness, in Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency (ACM, New York, 2020), pp. 501–512. https://doi.org/10.1145/3351095.3372826

    Google Scholar 

  37. J. Harambam, D. Bountouridis, M. Makhortykh, J. van Hoboken, Designing for the better by taking users into account: a qualitative evaluation of user control mechanisms in (news) recommender systems, in Proceedings of the 13th ACM Conference on Recommender Systems (ACM, New York, 2019), pp. 69–77. https://doi.org/10.1145/3298689.3347014

    Google Scholar 

  38. M. Hardt, E. Price, N. Srebro, Equality of opportunity in supervised learning, in Advances in Neural Information Processing Systems (2016), pp. 3315–3323

    Google Scholar 

  39. F.M. Harper, J.A. Konstan, The MovieLens datasets: history and context. ACM Trans. Interact. Intell. Syst. 5(4), 19:1–19:19 (2015). https://doi.org/10.1145/2827872

  40. T. Hashimoto, M. Srivastava, H. Namkoong, P. Liang, Fairness without demographics in repeated loss minimization, in Proceedings of the 35th International Conference on Machine Learning, Stockholmsmässan, Stockholm Sweden, PMLR, vol. 80, ed. by J. Dy, A. Krause (2018), pp. 1929–1938

    Google Scholar 

  41. T. Hentschel, S. Braun, C.V. Peus, D. Frey, Wording of advertisements influences women’s intention to apply for career opportunities. Acad. Manag. Proc. 2014(1), 15994 (2014). https://doi.org/10.5465/ambpp.2014.15994abstract

  42. A.L. Hoffmann, Where fairness fails: data, algorithms, and the limits of antidiscrimination discourse. Inf. Commun. Soc. 22(7), 900–915 (2019). https://doi.org/10.1080/1369118X.2019.1573912

    Article  Google Scholar 

  43. B. Hutchinson, M. Mitchell, 50 years of test (un)fairness: lessons for machine learning, in FAT 2019: Proceedings of the Conference on Fairness, Accountability, and Transparency (ACM, New York, 2019), pp. 49–58. https://doi.org/10.1145/3287560.3287600

    Google Scholar 

  44. N. Kallus, X. Mao, A. Zhou, Assessing algorithmic fairness with unobserved protected class using data combination. Preprint (2019). https://doi.org/1906.00285

  45. T. Kamishima, S. Akaho, Considerations on recommendation independence for a Find-Good-Items task, in Workshop on Fairness, Accountability and Transparency in Recommender Systems at RecSys 2017 (2017)

    Google Scholar 

  46. T. Kamishima, S. Akaho, H. Asoh, J. Sakuma, Recommendation independence, in Proceedings of the 1st Conference on Fairness, Accountability and Transparency, PMLR, vol. 81, ed. by S.A. Friedler, C. Wilson (2018), pp. 187–201

    Google Scholar 

  47. C. Karako, P. Manggala, Using image fairness representations in Diversity-Based re-ranking for recommendations, in Adjunct Publication of the 26th Conference on User Modeling, Adaptation and Personalization (ACM, New York, 2018), pp. 23–28. https://doi.org/10.1145/3213586.3226206

    Google Scholar 

  48. M. Kaya, D. Bridge, N. Tintarev, Ensuring fairness in group recommendations by Rank-Sensitive balancing of relevance, in Fourteenth ACM Conference on Recommender Systems (ACM, New York, 2020), pp. 101–110, https://doi.org/10.1145/3383313.3412232

    Google Scholar 

  49. M. Kearns, S. Neel, A. Roth, Z.S. Wu, An empirical study of rich subgroup fairness for machine learning, in Proceedings of the Conference on Fairness, Accountability, and Transparency (ACM, New York, 2019), pp. 100–109. https://doi.org/10.1145/3287560.3287592

    Book  Google Scholar 

  50. P. Lahoti, K.P. Gummadi, G. Weikum, iFair: learning individually fair data representations for algorithmic decision making, in 2019 IEEE 35th International Conference on Data Engineering (ICDE) (2019), pp. 1334–1345. https://doi.org/10.1109/ICDE.2019.00121

  51. W. Liu, J. Guo, N. Sonboli, R. Burke, S. Zhang, Personalized fairness-aware re-ranking for microlending, in Proceedings of the 13th ACM Conference on Recommender Systems (ACM, New York, 2019). https://doi.org/10.1145/3298689.3347016

    Google Scholar 

  52. R. Mehrotra, A. Anderson, F. Diaz, A. Sharma, H. Wallach, E. Yilmaz, Auditing search engines for differential satisfaction across demographics, in Proceedings of the 26th International Conference on World Wide Web Companion, International World Wide Web Conferences Steering Committee, Republic and Canton of Geneva (2017), pp. 626–633. https://doi.org/10.1145/3041021.3054197

  53. M. Mitchell, S. Wu, A. Zaldivar, P. Barnes, L. Vasserman, B. Hutchinson, E. Spitzer, I.D. Raji, T. Gebru, Model cards for model reporting, in Proceedings of the Conference on Fairness, Accountability, and Transparency (ACM, New York, 2019), pp. 220–229. https://doi.org/10.1145/3287560.3287596

    Book  Google Scholar 

  54. S. Mitchell, E. Potash, S. Barocas, A. D’Amour, K. Lum, Algorithmic fairness: choices, assumptions, and definitions. Annu. Rev. Stat. Appl. 8 (2020). https://doi.org/10.1146/annurev-statistics-042720-125902

  55. N. Modani, D. Jain, U. Soni, G.K. Gupta, P. Agarwal, Fairness aware recommendations on Behance, in Advances in Knowledge Discovery and Data Mining (Springer International Publishing, 2017), pp. 144–155. https://doi.org/10.1007/978-3-319-57529-2_12

  56. M. Nasr, M.C. Tschantz, Bidding strategies with gender nondiscrimination constraints for online ad auctions, in Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency (ACM, New York, 2020), pp. 337–347. https://doi.org/10.1145/3351095.3375783

    Google Scholar 

  57. A. Olteanu, C. Castillo, F. Diaz, E. Kıcıman, Social data: biases, methodological pitfalls, and ethical boundaries. Front. Big Data 2, 13 (2019). https://doi.org/10.3389/fdata.2019.00013

    Article  Google Scholar 

  58. S. Rendle, C. Freudenthaler, Z. Gantner, L. Schmidt-Thieme, BPR: Bayesian personalized ranking from implicit feedback, in Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence (AUAI Press, Arlington, 2009), pp. 452–461

    Google Scholar 

  59. R.L.T. Santos, J. Peng, C. Macdonald, I. Ounis, Explicit search result diversification through sub-queries, in ECIR 2010: Advances in Information Retrieval. LNCS, vol. 5993 (Springer, 2010), pp. 87–99. https://doi.org/10.1007/978-3-642-12275-0_11

  60. P. Sapiezynski, W. Zeng, E.R. Robertson, A. Mislove, C. Wilson, Quantifying the impact of user attention on fair group representation in ranked lists, in Companion Proceedings of The 2019 World Wide Web Conference (ACM, New York, 2019), pp. 553–562. https://doi.org/10.1145/3308560.3317595

    Book  Google Scholar 

  61. M. Schedl, The LFM-1b dataset for music retrieval and recommendation, in Proceedings of the 2016 ACM on International Conference on Multimedia Retrieval (ACM, New York, 2016), pp. 103–110. https://doi.org/10.1145/2911996.2912004

    Book  Google Scholar 

  62. A.D. Selbst, D. Boyd, S.A. Friedler, S. Venkatasubramanian, J. Vertesi, Fairness and abstraction in sociotechnical systems, in Proceedings of the Conference on Fairness, Accountability, and Transparency - FAT* ’19 (ACM, New York, 2019), pp. 59–68. https://doi.org/10.1145/3287560.3287598

    Book  Google Scholar 

  63. A. Singh, T. Joachims, Policy learning for fairness in ranking, in Advances in Neural Information Processing Systems, vol. 32, ed. by H. Wallach, H. Larochelle, A. Beygelzimer, F. d’Alché Buc, E. Fox, R. Garnett (2019), pp. 5426–5436

    Google Scholar 

  64. N. Sonboli, R. Burke, N. Mattei, F. Eskandanian, T. Gao, “and the winner is…”: dynamic lotteries for multi-group fairness-aware recommendation. Preprint (2020). https://doi.org/2009.02590

  65. N. Sonboli, F. Eskandanian, R. Burke, W. Liu, B. Mobasher, Opportunistic multi-aspect fairness through personalized re-ranking, in Proceedings of the 28th ACM Conference on User Modeling, Adaptation and Personalization (ACM, New York, 2020), pp. 239–247. https://doi.org/10.1145/3340631.3394846

    Google Scholar 

  66. N. Sonboli, J.J. Smith, F. Cabral Berenfus, R. Burke, C. Fiesler, Fairness and transparency in recommendation: the users’ perspective, in Proceedings of the 29th ACM Conference on User Modeling, Adaptation and Personalization (2021), pp. 274–279. https://doi.org/10.1145/3450613.3456835

  67. H. Steck, Calibrated recommendations, in Proceedings of the 12th ACM Conference on Recommender Systems (ACM, 2018), pp. 154–162. https://doi.org/10.1145/3240323.3240372

  68. Ö. Sürer, R. Burke, E.C. Malthouse, Multistakeholder recommendation with provider constraints, in Proceedings of the 12th ACM Conference on Recommender Systems (ACM, New York, 2018), pp. 54–62. https://doi.org/10.1145/3240323.3240350

    Book  Google Scholar 

  69. A. Xiang, I.D. Raji, On the legal compatibility of fairness definitions. Preprint (2019). https://doi.org/1912.00761

  70. K. Yang, J. Stoyanovich, Measuring fairness in ranked outputs, in Proceedings of the 29th International Conference on Scientific and Statistical Database Management (ACM, New York, 2017), Article 22, pp. 1–6. https://doi.org/10.1145/3085504.3085526

  71. K. Yang, J. Stoyanovich, A. Asudeh, B. Howe, H.V. Jagadish, G. Miklau, A nutritional label for rankings, in Proceedings of the 2018 International Conference on Management of Data - SIGMOD ’18 (ACM, New York, 2018), pp. 1773–1776. https://doi.org/10.1145/3183713.3193568

    Google Scholar 

  72. K. Yang, J.R. Loftus, J. Stoyanovich, Causal intersectionality for fair ranking. Preprint (2020). http://doi.org/2006.08688

  73. S. Yao, B. Huang, Beyond parity: fairness objectives for collaborative filtering, in Advances in Neural Information Processing Systems, vol. 30, ed. by I. Guyon, U.V. Luxburg, S. Bengio, H. Wallach, R. Fergus, S. Vishwanathan, R. Garnett (2017), pp. 2925–2934

    Google Scholar 

  74. M. Zehlike, F. Bonchi, C. Castillo, S. Hajian, M. Megahed, R. Baeza-Yates, FA*IR: a fair top-k ranking algorithm, in Proceedings of the 2017 ACM on Conference on Information and Knowledge Management (ACM, 2017), pp. 1569–1578. https://doi.org/10.1145/3132847.3132938

  75. X. Zhang, M. Khaliligarekani, C. Tekin, M. Liu, Group retention when using machine learning in sequential decision making: the interplay between user dynamics and fairness, in Advances in Neural Information Processing Systems, vol. 32, ed. by H. Wallach, H. Larochelle, A. Beygelzimer, F. d’Alché Buc, E. Fox, R. Garnett (2019), pp. 15269–15278

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. People and Information Research Team, Boise State University, Boise, ID, USA

    Michael D. Ekstrand

  2. School of Information, University of Texas at Austin, Austin, TX, USA

    Anubrata Das

  3. That Recommender Systems Lab, University of Colorado Boulder, Boulder, CO, USA

    Robin Burke

  4. Microsoft Research, Montreal, QC, Canada

    Fernando Diaz

Authors
  1. Michael D. Ekstrand
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Anubrata Das
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Robin Burke
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Fernando Diaz
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Michael D. Ekstrand .

Editor information

Editors and Affiliations

  1. Faculty of Computer Science, Free University of Bozen-Bolzano, Bozen-Bolzano, Italy

    Francesco Ricci

  2. Software and Information Systems Engineering, Ben-Gurion University of the Negev, Beer-Sheva, Israel

    Lior Rokach

  3. Software and Information Systems Engineering, Ben-Gurion University of the Negev, Beer-Sheva, Israel

    Bracha Shapira

Rights and permissions

Reprints and permissions

Copyright information

© 2022 Springer Science+Business Media, LLC, part of Springer Nature

About this chapter

Cite this chapter

Ekstrand, M.D., Das, A., Burke, R., Diaz, F. (2022). Fairness in Recommender Systems. In: Ricci, F., Rokach, L., Shapira, B. (eds) Recommender Systems Handbook. Springer, New York, NY. https://doi.org/10.1007/978-1-0716-2197-4_18

Download citation

  • DOI: https://doi.org/10.1007/978-1-0716-2197-4_18

  • Published:

  • Publisher Name: Springer, New York, NY

  • Print ISBN: 978-1-0716-2196-7

  • Online ISBN: 978-1-0716-2197-4

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation