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Toward Fair Recommendation in Two-sided Platforms

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Published:21 December 2021Publication History
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Abstract

Many online platforms today (such as Amazon, Netflix, Spotify, LinkedIn, and AirBnB) can be thought of as two-sided markets with producers and customers of goods and services. Traditionally, recommendation services in these platforms have focused on maximizing customer satisfaction by tailoring the results according to the personalized preferences of individual customers. However, our investigation reinforces the fact that such customer-centric design of these services may lead to unfair distribution of exposure to the producers, which may adversely impact their well-being. However, a pure producer-centric design might become unfair to the customers. As more and more people are depending on such platforms to earn a living, it is important to ensure fairness to both producers and customers. In this work, by mapping a fair personalized recommendation problem to a constrained version of the problem of fairly allocating indivisible goods, we propose to provide fairness guarantees for both sides. Formally, our proposed FairRec algorithm guarantees Maxi-Min Share of exposure for the producers, and Envy-Free up to One Item fairness for the customers. Extensive evaluations over multiple real-world datasets show the effectiveness of FairRec in ensuring two-sided fairness while incurring a marginal loss in overall recommendation quality. Finally, we present a modification of FairRec (named as FairRecPlus) that at the cost of additional computation time, improves the recommendation performance for the customers, while maintaining the same fairness guarantees.

REFERENCES

  1. [1] Abdollahpouri Himan and Burke Robin. 2019. Multi-stakeholder recommendation and its connection to multi-sided fairness. Retrieved from https://arXiv:1907.13158.Google ScholarGoogle Scholar
  2. [2] Abdollahpouri Himan, Burke Robin, and Mobasher Bamshad. 2017. Controlling popularity bias in learning-to-rank recommendation. In Proceedings of the 11th ACM Conference on Recommender Systems. ACM, 4246. Google ScholarGoogle ScholarDigital LibraryDigital Library
  3. [3] Abdollahpouri Himan, Burke Robin, and Mobasher Bamshad. 2019. Managing popularity bias in recommender systems with personalized re-ranking. Retrieved from https://arXiv:1901.07555.Google ScholarGoogle Scholar
  4. [4] Agarwal Aman, Zaitsev Ivan, Wang Xuanhui, Li Cheng, Najork Marc, and Joachims Thorsten. 2019. Estimating position bias without intrusive interventions. In Proceedings of the 12th ACM International Conference on Web Search and Data Mining. ACM, 474482. Google ScholarGoogle ScholarDigital LibraryDigital Library
  5. [5] AirBnb. 2019. Retrieved from https://blog.atairbnb.com/the-airbnb-community-commitment/.Google ScholarGoogle Scholar
  6. [6] AirBnb. 2019. Retrieved from https://www.airbnb.com/help/article/2474/what-is-the-earnings-guarantee-prog-ramme-for-new-hosts.Google ScholarGoogle Scholar
  7. [7] Amanatidis Georgios, Birmpas Georgios, and Markakis Evangelos. 2018. Comparing approximate relaxations of envy-freeness. In Proceedings of the International Joint Conference on Artificial Intelligence (IJCAI’18). IJCAI, 4248. Google ScholarGoogle ScholarDigital LibraryDigital Library
  8. [8] Amanatidis Georgios, Markakis Evangelos, Nikzad Afshin, and Saberi Amin. 2015. Approximation algorithms for computing maximin share allocations. In Proceedings of the International Colloquium on Automata, Languages, and Programming (ICALP’15). 3951.Google ScholarGoogle ScholarCross RefCross Ref
  9. [9] Banerjee Ashmi, Patro Gourab K., Dietz Linus W., and Chakraborty Abhijnan. 2020. Analyzing “Near Me” Services: Potential for Exposure Bias in Location-based Retrieval. In Proceedings of the IEEE International Conference on Big Data (Big Data’20). IEEE, 36423651.Google ScholarGoogle ScholarCross RefCross Ref
  10. [10] Barman Siddharth, Biswas Arpita, Krishnamurthy Sanath Kumar, and Narahari Y.. 2018. Groupwise maximin fair allocation of indivisible goods. In Proceedings of the AAAI Conference on Artificial Intelligence. 917924. Google ScholarGoogle ScholarDigital LibraryDigital Library
  11. [11] Barman Siddharth and Krishnamurthy Sanath Kumar. 2017. Approximation algorithms for maximin fair division. In Proceedings of the ACM Conference on Economics and Computation (EC’17). ACM, 647664. Google ScholarGoogle ScholarDigital LibraryDigital Library
  12. [12] Biega Asia J., Gummadi Krishna P., and Weikum Gerhard. 2018. Equity of attention: Amortizing individual fairness in rankings. Retrieved from https://arXiv:1805.01788.Google ScholarGoogle Scholar
  13. [13] Bilò Vittorio, Caragiannis Ioannis, Flammini Michele, Igarashi Ayumi, Monaco Gianpiero, Peters Dominik, Vinci Cosimo, and Zwicker William S.. 2019. Almost envy-free allocations with connected bundles. In Proceedings of the 11th Innovations in Theoretical Computer Science (ITCS’19).Google ScholarGoogle Scholar
  14. [14] Biswas Arpita and Barman Siddharth. 2018. Fair division under cardinality constraints. In Proceedings of the 27th International Joint Conference on Artificial Intelligence (IJCAI’18). ijcai.org, 9197. https://doi.org/10.24963/ijcai.2018/13 Google ScholarGoogle ScholarDigital LibraryDigital Library
  15. [15] Biswas Arpita and Barman Siddharth. 2019. Matroid constrained fair allocation problem. In Proceedings of the 33rd AAAI Conference on Artificial Intelligence. AAAI Press, 99219922. https://doi.org/10.1609/aaai.v33i01.33019921 Google ScholarGoogle ScholarDigital LibraryDigital Library
  16. [16] Bouveret Sylvain, Cechlárová Katarína, Elkind Edith, Igarashi Ayumi, and Peters Dominik. 2017. Fair division of a graph. In Proceedings of the International Joint Conference on Artificial Intelligence (IJCAI’17). 135141. Google ScholarGoogle ScholarDigital LibraryDigital Library
  17. [17] Bouveret Sylvain and Lemaître Michel. 2014. Characterizing conflicts in fair division of indivisible goods using a scale of criteria. In Proceedings of the International Conference on Autonomous Agents and Multi-Agent Systems. 13211328. Google ScholarGoogle ScholarDigital LibraryDigital Library
  18. [18] Brandt Felix, Conitzer Vincent, Endriss Ulle, Lang Jérôme, and Procaccia Ariel D.. 2016. Handbook of Computational Social Choice. Cambridge University Press. DOI: DOI: https://doi.org/10.1017/CBO9781107446984 Google ScholarGoogle ScholarCross RefCross Ref
  19. [19] Budish Eric. 2011. The combinatorial assignment problem: Approximate competitive equilibrium from equal incomes. J. Politic. Econ. 119, 6 (2011), 10611103.Google ScholarGoogle ScholarCross RefCross Ref
  20. [20] Burke Robin. 2017. Multisided fairness for recommendation. Retrieved from https://arXiv:1707.00093.Google ScholarGoogle Scholar
  21. [21] Cantador Iván, Brusilovsky Peter, and Kuflik Tsvi. 2011. 2nd workshop on information heterogeneity and fusion in recommender systems (HetRec 2011). In Proceedings of the 5th ACM conference on Recommender systems (RecSys’11). ACM, New York, NY. Google ScholarGoogle ScholarDigital LibraryDigital Library
  22. [22] Caragiannis Ioannis, Kurokawa David, Moulin Hervé, Procaccia Ariel D., Shah Nisarg, and Wang Junxing. 2016. The unreasonable fairness of maximum Nash welfare. In Proceedings of the ACM Conference on Economics and Computation (EC’16). ACM, 305322. Google ScholarGoogle ScholarDigital LibraryDigital Library
  23. [23] Chakraborty Abhijnan, Hannak Aniko, Biega Asia J., and Gummadi Krishna P.. 2017. Fair sharing for sharing economy platforms.Google ScholarGoogle Scholar
  24. [24] Chakraborty Abhijnan, Patro Gourab K., Ganguly Niloy, Gummadi Krishna P., and Loiseau Patrick. 2019. Equality of voice: Towards fair representation in crowdsourced top-k recommendations. Google ScholarGoogle ScholarDigital LibraryDigital Library
  25. [25] CNBC.com. 2017. Retrieved from https://www.cnbc.com/2017/04/20/only-4-percent-of-uber-drivers-remain-after-a-year-says-report.html.Google ScholarGoogle Scholar
  26. [26] Dash Abhisek, Chakraborty Abhijnan, Ghosh Saptarshi, Mukherjee Animesh, and Gummadi Krishna P.. 2021. When the umpire is also a player: Bias in private label product recommendations on e-commerce marketplaces. In Proceedings of the ACM Conference on Fairness, Accountability, and Transparency. 873884. Google ScholarGoogle ScholarDigital LibraryDigital Library
  27. [27] Edelman Benjamin, Luca Michael, and Svirsky Dan. 2017. Racial discrimination in the sharing economy: Evidence from a field experiment. Amer. Econ. J.: Appl. Econ. 9, 2 (2017).Google ScholarGoogle ScholarCross RefCross Ref
  28. [28] Endriss Ulle. 2017. Trends in Computational Social Choice. Lulu.com. Google ScholarGoogle ScholarDigital LibraryDigital Library
  29. [29] Engbom Niklas and Moser Christian. 2018. Earnings inequality and the minimum wage: Evidence from brazil. Federal Reserve Bank of Minneapolis-Opportunity and Inclusive Growth Institute Working Paper 7 (2018), 1850.Google ScholarGoogle Scholar
  30. [30] Falk Armin, Fehr Ernst, and Zehnder Christian. 2006. Fairness perceptions and reservation wages’the behavioral effects of minimum wage laws. Quart. J. Econ. 121, 4 (2006), 13471381.Google ScholarGoogle Scholar
  31. [31] Foley D. C.. 1967. Resource allocation in the public sector. Yale Econ. Essays 7 (1967), 7376.Google ScholarGoogle Scholar
  32. [32] Geyik Sahin Cem, Ambler Stuart, and Kenthapadi Krishnaram. 2019. Fairness-aware ranking in search & recommendation systems with application to linkedin talent search. In Proceedings of the ACM International Conference on Knowledge Discovery and Data Mining (KDD’19). Google ScholarGoogle ScholarDigital LibraryDigital Library
  33. [33] Ghodsi Mohammad, HajiAghayi MohammadTaghi, Seddighin Masoud, Seddighin Saeed, and Yami Hadi. 2018. Fair allocation of indivisible goods: Improvements and generalizations. In Proceedings of the ACM Conference on Economics and Computation. ACM, 539556. Google ScholarGoogle ScholarDigital LibraryDigital Library
  34. [34] Gourvès Laurent and Monnot Jérôme. 2017. Approximate maximin share allocations in matroids. In Proceedings of the 10th International Conference on Algorithms and Complexity (CIAC’17). 310321. DOI: DOI: https://doi.org/10.1007/978-3-319-57586-5_26Google ScholarGoogle ScholarCross RefCross Ref
  35. [35] Gourvès Laurent, Monnot Jérôme, and Tlilane Lydia. 2014. Near fairness in matroids. In Proceedings of the 21st European Conference on Artificial Intelligence (ECAI’14). IOS Press, 393398. https://doi.org/10.3233/978-1-61499-419-0-393 Google ScholarGoogle ScholarDigital LibraryDigital Library
  36. [36] Graham Mark, Hjorth Isis, and Lehdonvirta Vili. 2017. Digital labour and development: Impacts of global digital labour platforms and the gig economy on worker livelihoods. Transfer: Eur. Rev. Labour Res. 23, 2 (2017), 135162.Google ScholarGoogle ScholarCross RefCross Ref
  37. [37] Green David A. and Harrison Kathryn. 2010. Minimum Wage Setting and Standards of Fairness. Technical Report. IFS working papers.Google ScholarGoogle ScholarCross RefCross Ref
  38. [38] Hannák Anikó, Wagner Claudia, Garcia David, Mislove Alan, Strohmaier Markus, and Wilson Christo. 2017. Bias in online freelance marketplaces: Evidence from taskrabbit and fiverr. In Proceedings of the ACM Conference on Computer-Supported Cooperative Work and Social Computing (CSCW’17). Google ScholarGoogle ScholarDigital LibraryDigital Library
  39. [39] He Ruining, Kang Wang-Cheng, and McAuley Julian. 2017. Translation-based recommendation. In Proceedings of the 11th ACM Conference on Recommender Systems. ACM, 161169. Google ScholarGoogle ScholarDigital LibraryDigital Library
  40. [40] Jannach Dietmar, Lerche Lukas, Kamehkhosh Iman, and Jugovac Michael. 2015. What recommenders recommend: An analysis of recommendation biases and possible countermeasures. User Model. User-Adapt. Interact. 25, 5 (2015), 427491. Google ScholarGoogle ScholarDigital LibraryDigital Library
  41. [41] Kamishima Toshihiro, Akaho Shotaro, Asoh Hideki, and Sakuma Jun. 2014. Correcting popularity bias by enhancing recommendation neutrality. In Poster Proceedings of the 8th ACM Conference on Recommender Systems, RecSys 2014, Foster City, Silicon Valley, CA, USA, Li Chen and Jalal Mahmud (Eds.). CEUR-WS.org. http://ceur-ws.org/Vol-1247.Google ScholarGoogle Scholar
  42. [42] Kim Donghyun, Park Chanyoung, Oh Jinoh, Lee Sungyoung, and Yu Hwanjo. 2016. Convolutional matrix factorization for document context-aware recommendation. In Proceedings of the 10th ACM Conference on Recommender Systems. 233240. Google ScholarGoogle ScholarDigital LibraryDigital Library
  43. [43] Koren Yehuda, Bell Robert, and Volinsky Chris. 2009. Matrix factorization techniques for recommender systems. Computer 8 (2009), 3037. Google ScholarGoogle ScholarDigital LibraryDigital Library
  44. [44] Kurokawa David, Procaccia Ariel D., and Wang Junxing. 2016. When can the maximin share guarantee be guaranteed? In Proceedings of the AAAI Conference on Artificial Intelligence. AAAI, 523529. Google ScholarGoogle ScholarDigital LibraryDigital Library
  45. [45] Lambrecht Anja and Tucker Catherine. 2019. Algorithmic bias? An empirical study of apparent gender-based discrimination in the display of STEM career ads. Manage. Sci. 65, 7 (2019), 29662981. DOI: 10.1287/mnsc.2018.3093Google ScholarGoogle ScholarDigital LibraryDigital Library
  46. [46] Liang Dawen, Altosaar Jaan, Charlin Laurent, and Blei David M.. 2016. Factorization meets the item embedding: Regularizing matrix factorization with item co-occurrence. In Proceedings of the 10th ACM Conference on Recommender Systems. ACM, 5966. Google ScholarGoogle ScholarDigital LibraryDigital Library
  47. [47] Lin Carl and Yun Myeong-Su. 2016. The effects of the minimum wage on earnings inequality: Evidence from China. In Income Inequality Around the World. Emerald Group Publishing Limited, 179212.Google ScholarGoogle Scholar
  48. [48] Lipton Richard J., Markakis Evangelos, Mossel Elchanan, and Saberi Amin. 2004. On approximately fair allocations of indivisible goods. In Proceedings of the 5th ACM Conference on Electronic Commerce (EC’04). ACM, 125131. https://doi.org/10.1145/988772.988792 Google ScholarGoogle ScholarDigital LibraryDigital Library
  49. [49] Medium.com. 2019. Retrieved from https://medium.com/public-market/in-their-own-words-why-sellers-are-fed-up-with-amazon-e97da44f7f18.Google ScholarGoogle Scholar
  50. [50] NewYorkTimes. 2019. Retrieved from https://www.nytimes.com/2019/09/09/business/economy/uber-lyft-california.html.Google ScholarGoogle Scholar
  51. [51] Ning Xia, Desrosiers Christian, and Karypis George. 2015. A comprehensive survey of neighborhood-based recommendation methods. In Recommender Systems Handbook. Springer, 3776.Google ScholarGoogle ScholarCross RefCross Ref
  52. [52] Patro Gourab K., Biswas Arpita, Ganguly Niloy, Gummadi Krishna P., and Chakraborty Abhijnan. 2020. FairRec: Two-sided fairness for personalized recommendations in two-sided platforms. In Proceedings of the Web Conference. 11941204. Google ScholarGoogle ScholarDigital LibraryDigital Library
  53. [53] Patro Gourab K., Chakraborty Abhijnan, Ganguly Niloy, and Gummadi Krishna P.. 2020. Incremental fairness in two-sided market platforms: On smoothly updating recommendations. In Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 34. 181188.Google ScholarGoogle ScholarCross RefCross Ref
  54. [54] Pollin Robert, Brenner Mark, Luce Stephanie, and Wicks-Lim Jeannette. 2008. A Measure of Fairness: The Economics of Living Wages and Minimum Wages in the United States. Cornell University Press.Google ScholarGoogle ScholarCross RefCross Ref
  55. [55] Procaccia Ariel D. and Wang Junxing. 2014. Fair enough: Guaranteeing approximate maximin shares. In ACM Conference on Economics and Computation, EC. 675692. Google ScholarGoogle ScholarDigital LibraryDigital Library
  56. [56] Raj Amifa, Wood Connor, Montoly Ananda, and Ekstrand Michael D.. 2020. Comparing fair ranking metrics. Retrieved from https://arXiv:2009.01311.Google ScholarGoogle Scholar
  57. [57] Salganik Matthew J., Dodds Peter Sheridan, and Watts Duncan J.. 2006. Experimental study of inequality and unpredictability in an artificial cultural market. Science 311, 5762 (2006), 854856. DOI: DOI: https://doi.org/10.1126/science.1121066Google ScholarGoogle ScholarCross RefCross Ref
  58. [58] Serbos Dimitris, Qi Shuyao, Mamoulis Nikos, Pitoura Evaggelia, and Tsaparas Panayiotis. 2017. Fairness in package-to-group recommendations. In Proceedings of the World Wide Web Conference (WWW’17).Google ScholarGoogle ScholarDigital LibraryDigital Library
  59. [59] Singh Ashudeep and Joachims Thorsten. 2018. Fairness of exposure in rankings. In Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 22192228. Google ScholarGoogle ScholarDigital LibraryDigital Library
  60. [60] Slate.com. 2019. Retrieved from https://slate.com/technology/2019/06/billion-dollar-bully-documentary-yelp.html.Google ScholarGoogle Scholar
  61. [61] Steinhaus Hugo. 1948. The problem of fair division. Econometrica 16 (1948), 101104.Google ScholarGoogle Scholar
  62. [62] Stromquist Walter. 1980. How to cut a cake fairly. Amer. Mathe. Monthly 87, 8 (1980), 640644.Google ScholarGoogle ScholarCross RefCross Ref
  63. [63] Sühr Tom, Biega Asia J., Zehlike Meike, Gummadi Krishna P., and Chakraborty Abhijnan. 2019. Two-sided fairness for repeated matchings in two-sided markets: A case study of a ride-hailing platform. In Proceedings of the ACM International Conference on Knowledge Discovery and Data Mining (KDD’19). Google ScholarGoogle ScholarDigital LibraryDigital Library
  64. [64] Sürer Özge, Burke Robin, and Malthouse Edward C.. 2018. Multistakeholder recommendation with provider constraints. In Proceedings of the 12th ACM Conference on Recommender Systems. 5462. Google ScholarGoogle ScholarDigital LibraryDigital Library
  65. [65] Varian Hal R.. 1974. Equity, envy, and efficiency. J. Econ. Theory 9, 1 (1974), 6391.Google ScholarGoogle ScholarCross RefCross Ref
  66. [66] Wired.com. 2019. Retrieved from https://www.wired.com/story/europes-new-rules-aim-make-online-marketplaces-more-fair/.Google ScholarGoogle Scholar
  67. [67] Xue Hong-Jian, Dai Xinyu, Zhang Jianbing, Huang Shujian, and Chen Jiajun. 2017. Deep matrix factorization models for recommender systems. In Proceedings of the International Joint Conference on Artificial Intelligence (IJCAI’17), Vol. 17. 32033209. Google ScholarGoogle ScholarDigital LibraryDigital Library
  68. [68] Zhou Ke, Yang Shuang-Hong, and Zha Hongyuan. 2011. Functional matrix factorizations for cold-start recommendation. In Proceedings of the 34th International ACM SIGIR Conference on Research and Development in Information Retrieval. 315324. Google ScholarGoogle ScholarDigital LibraryDigital Library

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      cover image ACM Transactions on the Web
      ACM Transactions on the Web  Volume 16, Issue 2
      May 2022
      148 pages
      ISSN:1559-1131
      EISSN:1559-114X
      DOI:10.1145/3506669
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      Publication History

      • Published: 21 December 2021
      • Revised: 1 October 2021
      • Accepted: 1 October 2021
      • Received: 1 November 2020
      Published in tweb Volume 16, Issue 2

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