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Fair Regret Minimization Queries

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Intelligent Data Engineering and Automated Learning – IDEAL 2021 (IDEAL 2021)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 13113))

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Abstract

When facing a database containing numerous tuples, users may be only interested in a small but representative subset. Unlike top-k and skyline queries, the k-regret query is a tool which does not need users to provide preferences but returns a representative subset of specified size by users with the minimum regret. However, existing regret-based approaches cannot answer the k-regret query on the dataset which is divided into groups and the result set contains fixed-size tuples in each group, which can be viewed as a metric of fairness. For this scenario, in this paper we generalize the k-regret query to its fair form, i.e., the fair regret minimization query. Moreover, we provide an efficient algorithm named \(\alpha \)-Greedy which does not need to access the whole dataset at each greedy step with the help of a layer structure. We conduct experiments to verify the efficiency of the proposed algorithm on both synthetic and real datasets.

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Notes

  1. 1.

    We use the terms “database”,“dataset” and “tuple”,“point” interchangeably in the paper.

  2. 2.

    https://www.basketball-reference.com/.

  3. 3.

    http://www.ipums.org.

  4. 4.

    http://archive.ics.uci.edu/ml/datasets/El+Nino.

References

  1. Agarwal, P.K., Kumar, N., Sintos, S., Suri, S.: Efficient algorithms for k-regret minimizing sets. In: Proceedings of International Symposium on Experimental Algorithms (SEA), pp. 7:1–7:23 (2017)

    Google Scholar 

  2. Ajtai, M., Aspnes, J., Naor, M., Rabani, Y., Schulman, L.J., Waarts, O.: Fairness in scheduling. J. Algorithms 29(2), 306–357 (1998)

    Article  MathSciNet  Google Scholar 

  3. Asudeh, A., Jagadish, H.: Fairly evaluating and scoring items in a data set. In: Proceedings of the VLDB Endowment (VLDB), pp. 3445–3448 (2020)

    Google Scholar 

  4. Asudeh, A., Nazi, A., Zhang, N., Das, G.: Efficient computation of regret-ratio minimizing set: a compact maxima representative. In: Proceedings of International Conference on Management of Data (SIGMOD), pp. 821–834 (2017)

    Google Scholar 

  5. Beutel, A., et al.: Fairness in recommendation ranking through pairwise comparisons. In: Proceedings of International Conference on Knowledge Discovery & Data Mining (SIGKDD), pp. 2212–2220 (2019)

    Google Scholar 

  6. Börzsöny, S., Kossmann, D., Stocker, K.: The skyline operator. In: Proceedings of International Conference on Data Engineering (ICDE), pp. 421–430 (2001)

    Google Scholar 

  7. Celis, L.E., Huang, L., Vishnoi, N.K.: Multiwinner voting with fairness constraints. In: Proceedings of International Joint Conference on Artificial Intelligence (IJCAI), pp. 144–151 (2018)

    Google Scholar 

  8. Celis, L.E., Keswani, V., Straszak, D., Deshpande, A., Kathuria, T., Vishnoi, N.: Fair and diverse DPP-based data summarization. In: Proceedings of International Conference on Machine Learning (ICML), pp. 716–725 (2018)

    Google Scholar 

  9. Celis, L.E., Straszak, D., Vishnoi, N.K.: Ranking with fairness constraints. In: International Colloquium on Automata, Languages, and Programming (ICALP), vol. 107, pp. 28:1–28:15 (2018)

    Google Scholar 

  10. Celis, L.E., Vishnoi, N.K.: Fair personalization. arXiv preprint arXiv:1707.02260 p. 7 (2017)

  11. Chester, S., Thomo, A., Venkatesh, S., Whitesides, S.: Computing k-regret minimizing sets. In: Proceedings of the VLDB Endowment (VLDB), pp. 389–400 (2014)

    Google Scholar 

  12. Chouldechova, A., Roth, A.: A snapshot of the frontiers of fairness in machine learning. Commun. ACM 63(5), 82–89 (2020)

    Article  Google Scholar 

  13. Dash, A., Shandilya, A., Biswas, A., Ghosh, K., Ghosh, S., Chakraborty, A.: Summarizing user-generated textual content: motivation and methods for fairness in algorithmic summaries. In: Proceedings of the ACM on Human-Computer Interaction, vol. 3(CSCW), pp. 1–28 (2019)

    Google Scholar 

  14. Dwork, C., Hardt, M., Pitassi, T., Reingold, O., Zemel, R.: Fairness through awareness. In: Proceedings of Innovations in Theoretical Computer Science Conference, pp. 214–226 (2012)

    Google Scholar 

  15. Faulkner, T.K., Brackenbury, W., Lall, A.: k-regret queries with nonlinear utilities. In: Proceedings of the VLDB Endowment (VLDB), pp. 2098–2109 (2015)

    Google Scholar 

  16. Feige, U.: A threshold of ln n for approximating set cover. J. ACM (JACM) 45(4), 634–652 (1998)

    Article  Google Scholar 

  17. Feige, U.: On allocations that maximize fairness. In: Proceedings of Symposium on Discrete Algorithms (SODA), vol. 8, pp. 287–293 (2008)

    Google Scholar 

  18. Kay, M., Matuszek, C., Munson, S.A.: Unequal representation and gender stereotypes in image search results for occupations. In: Proceedings of Conference on Human Factors in Computing Systems, pp. 3819–3828 (2015)

    Google Scholar 

  19. Kazemi, E., Zadimoghaddam, M., Karbasi, A.: Scalable deletion-robust submodular maximization: Data summarization with privacy and fairness constraints. In: Proceedings of International Conference on Machine Learning (ICML), pp. 2544–2553 (2018)

    Google Scholar 

  20. Nanongkai, D., Lall, A., Das Sarma, A., Makino, K.: Interactive regret minimization. In: Proceedings of International Conference on Management of Data (SIGMOD), pp. 109–120 (2012)

    Google Scholar 

  21. Nanongkai, D., Sarma, A.D., Lall, A., Lipton, R.J., Xu, J.: Regret-minimizing representative databases. In: Proceedings of the VLDB Endowment (VLDB), pp. 1114–1124 (2010)

    Google Scholar 

  22. Peng, P., Wong, R.C.W.: Geometry approach for k-regret query. In: Proceedings of International Conference on Data Engineering (ICDE), pp. 772–783 (2014)

    Google Scholar 

  23. Rabin, M.: Incorporating fairness into game theory and economics. Am. Econ. Rev. 83(5), 1281–1302 (1993)

    Google Scholar 

  24. Serbos, D., Qi, S., Mamoulis, N., Pitoura, E., Tsaparas, P.: Fairness in package-to-group recommendations. In: Proceedings of International Conference on World Wide Web (WWW), pp. 371–379 (2017)

    Google Scholar 

  25. Singh, A., Joachims, T.: Fairness of exposure in rankings. In: Proceedings of International Conference on Knowledge Discovery & Data Mining (SIGKDD), pp. 2219–2228 (2018)

    Google Scholar 

  26. Stoyanovich, J., Yang, K., Jagadish, H.: Online set selection with fairness and diversity constraints. In: Proceedings of International Conference on Extending Database Technology (EDBT), pp. 241–252 (2018)

    Google Scholar 

  27. Wang, Y., Fabbri, F., Mathioudakis, M.: Fair and representative subset selection from data streams. In: Proceedings of The Web Conference (WWW), p. 11 (2021)

    Google Scholar 

  28. Wang, Y., Li, Y., Wong, R.C.W., Tan, K.L.: A fully dynamic algorithm for k-regret minimizing sets. In: Proceedings of International Conference on Data Engineering (ICDE), p. 12 (2021)

    Google Scholar 

  29. Xie, M., Wong, R.C.W., Lall, A.: An experimental survey of regret minimization query and variants: bridging the best worlds between top-k query and skyline query. VLDB J. 29, 147–175 (2020)

    Article  Google Scholar 

  30. Xie, M., Wong, R.C.W., Li, J., Long, C., Lall, A.: Efficient k-regret query algorithm with restriction-free bound for any dimensionality. In: Proceedings of International Conference on Management of Data (SIGMOD), pp. 959–974 (2018)

    Google Scholar 

  31. Xie, M., Wong, R.C.W., Peng, P., Tsotras, V.J.: Being happy with the least: achieving \(\alpha \)-happiness with minimum number of tuples. In: Proceedings of International Conference on Data Engineering (ICDE), pp. 1009–1020 (2020)

    Google Scholar 

  32. Zafar, M.B., Valera, I., Rogriguez, M.G., Gummadi, K.P.: Fairness constraints: mechanisms for fair classification. In: Proceedings of International Conference on Artificial Intelligence and Statistics (AISTATS), pp. 962–970 (2017)

    Google Scholar 

  33. Zehlike, M., Yang, K., Stoyanovich, J.: Fairness in ranking: A survey. arXiv preprint arXiv:2103.14000 p. 58 (2021)

  34. Zeighami, S., Wong, R.C.: Minimizing average regret ratio in database. In: Proceedings of International Conference on Management of Data (SIGMOD), pp. 2265–2266 (2016)

    Google Scholar 

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Acknowledgment

This work is partially supported by the National Natural Science Foundation of China under grant U1733112 and the Fundamental Research Funds for the Central Universities under grant NS2020068.

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Authors and Affiliations

  1. College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing, China

    Yuan Ma & Jiping Zheng

  2. Collaborative Innovation Center of Novel Software Technology and Industrialization, Nanjing, China

    Jiping Zheng

Authors
  1. Yuan Ma
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  2. Jiping Zheng
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Corresponding author

Correspondence to Jiping Zheng .

Editor information

Editors and Affiliations

  1. University of Manchester, Manchester, UK

    Hujun Yin

  2. Universidad Politecnica de Madrid, Madrid, Spain

    David Camacho

  3. University of Birmingham, Birmingham, UK

    Peter Tino

  4. University of Manchester, Manchester, UK

    Richard Allmendinger

  5. University of Huelva, Huelva, Spain

    Antonio J. Tallón-Ballesteros

  6. Southern University of Science and Technology, Shenzhen, China

    Ke Tang

  7. Yonsei University, Seoul, Korea (Republic of)

    Sung-Bae Cho

  8. University of Minho, Braga, Portugal

    Paulo Novais

  9. NOVA University of Lisbon, Lisbon, Portugal

    Susana Nascimento

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Ma, Y., Zheng, J. (2021). Fair Regret Minimization Queries. In: Yin, H., et al. Intelligent Data Engineering and Automated Learning – IDEAL 2021. IDEAL 2021. Lecture Notes in Computer Science(), vol 13113. Springer, Cham. https://doi.org/10.1007/978-3-030-91608-4_51

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  • DOI: https://doi.org/10.1007/978-3-030-91608-4_51

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  • Print ISBN: 978-3-030-91607-7

  • Online ISBN: 978-3-030-91608-4

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