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.
- [1] . 2019. Multi-stakeholder recommendation and its connection to multi-sided fairness. Retrieved from https://arXiv:1907.13158.Google Scholar
- [2] . 2017. Controlling popularity bias in learning-to-rank recommendation. In Proceedings of the 11th ACM Conference on Recommender Systems. ACM, 42–46. Google Scholar
Digital Library
- [3] . 2019. Managing popularity bias in recommender systems with personalized re-ranking. Retrieved from https://arXiv:1901.07555.Google Scholar
- [4] . 2019. Estimating position bias without intrusive interventions. In Proceedings of the 12th ACM International Conference on Web Search and Data Mining. ACM, 474–482. Google Scholar
Digital Library
- [5] . 2019. Retrieved from https://blog.atairbnb.com/the-airbnb-community-commitment/.Google Scholar
- [6] . 2019. Retrieved from https://www.airbnb.com/help/article/2474/what-is-the-earnings-guarantee-prog-ramme-for-new-hosts.Google Scholar
- [7] . 2018. Comparing approximate relaxations of envy-freeness. In Proceedings of the International Joint Conference on Artificial Intelligence (IJCAI’18). IJCAI, 42–48. Google Scholar
Digital Library
- [8] . 2015. Approximation algorithms for computing maximin share allocations. In Proceedings of the International Colloquium on Automata, Languages, and Programming (ICALP’15). 39–51.Google Scholar
Cross Ref
- [9] . 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, 3642–3651.Google Scholar
Cross Ref
- [10] . 2018. Groupwise maximin fair allocation of indivisible goods. In Proceedings of the AAAI Conference on Artificial Intelligence. 917–924. Google Scholar
Digital Library
- [11] . 2017. Approximation algorithms for maximin fair division. In Proceedings of the ACM Conference on Economics and Computation (EC’17). ACM, 647–664. Google Scholar
Digital Library
- [12] . 2018. Equity of attention: Amortizing individual fairness in rankings. Retrieved from https://arXiv:1805.01788.Google Scholar
- [13] . 2019. Almost envy-free allocations with connected bundles. In Proceedings of the 11th Innovations in Theoretical Computer Science (ITCS’19).Google Scholar
- [14] . 2018. Fair division under cardinality constraints. In Proceedings of the 27th International Joint Conference on Artificial Intelligence (IJCAI’18). ijcai.org, 91–97. https://doi.org/10.24963/ijcai.2018/13 Google Scholar
Digital Library
- [15] . 2019. Matroid constrained fair allocation problem. In Proceedings of the 33rd AAAI Conference on Artificial Intelligence. AAAI Press, 9921–9922. https://doi.org/10.1609/aaai.v33i01.33019921 Google Scholar
Digital Library
- [16] . 2017. Fair division of a graph. In Proceedings of the International Joint Conference on Artificial Intelligence (IJCAI’17). 135–141. Google Scholar
Digital Library
- [17] . 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. 1321–1328. Google Scholar
Digital Library
- [18] . 2016. Handbook of Computational Social Choice. Cambridge University Press.
DOI: DOI: https://doi.org/10.1017/CBO9781107446984 Google ScholarCross Ref
- [19] . 2011. The combinatorial assignment problem: Approximate competitive equilibrium from equal incomes. J. Politic. Econ. 119, 6 (2011), 1061–1103.Google Scholar
Cross Ref
- [20] . 2017. Multisided fairness for recommendation. Retrieved from https://arXiv:1707.00093.Google Scholar
- [21] . 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 Scholar
Digital Library
- [22] . 2016. The unreasonable fairness of maximum Nash welfare. In Proceedings of the ACM Conference on Economics and Computation (EC’16). ACM, 305–322. Google Scholar
Digital Library
- [23] . 2017. Fair sharing for sharing economy platforms.Google Scholar
- [24] . 2019. Equality of voice: Towards fair representation in crowdsourced top-k recommendations. Google Scholar
Digital Library
- [25] . 2017. Retrieved from https://www.cnbc.com/2017/04/20/only-4-percent-of-uber-drivers-remain-after-a-year-says-report.html.Google Scholar
- [26] . 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. 873–884. Google Scholar
Digital Library
- [27] . 2017. Racial discrimination in the sharing economy: Evidence from a field experiment. Amer. Econ. J.: Appl. Econ. 9, 2 (2017).Google Scholar
Cross Ref
- [28] . 2017. Trends in Computational Social Choice. Lulu.com. Google Scholar
Digital Library
- [29] . 2018. Earnings inequality and the minimum wage: Evidence from brazil. Federal Reserve Bank of Minneapolis-Opportunity and Inclusive Growth Institute Working Paper 7 (2018), 18–50.Google Scholar
- [30] . 2006. Fairness perceptions and reservation wages’the behavioral effects of minimum wage laws. Quart. J. Econ. 121, 4 (2006), 1347–1381.Google Scholar
- [31] . 1967. Resource allocation in the public sector. Yale Econ. Essays 7 (1967), 73–76.Google Scholar
- [32] . 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 Scholar
Digital Library
- [33] . 2018. Fair allocation of indivisible goods: Improvements and generalizations. In Proceedings of the ACM Conference on Economics and Computation. ACM, 539–556. Google Scholar
Digital Library
- [34] . 2017. Approximate maximin share allocations in matroids. In Proceedings of the 10th International Conference on Algorithms and Complexity (CIAC’17). 310–321.
DOI: DOI: https://doi.org/10.1007/978-3-319-57586-5_26Google ScholarCross Ref
- [35] . 2014. Near fairness in matroids. In Proceedings of the 21st European Conference on Artificial Intelligence (ECAI’14). IOS Press, 393–398. https://doi.org/10.3233/978-1-61499-419-0-393 Google Scholar
Digital Library
- [36] . 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), 135–162.Google Scholar
Cross Ref
- [37] . 2010. Minimum Wage Setting and Standards of Fairness.
Technical Report . IFS working papers.Google ScholarCross Ref
- [38] . 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 Scholar
Digital Library
- [39] . 2017. Translation-based recommendation. In Proceedings of the 11th ACM Conference on Recommender Systems. ACM, 161–169. Google Scholar
Digital Library
- [40] . 2015. What recommenders recommend: An analysis of recommendation biases and possible countermeasures. User Model. User-Adapt. Interact. 25, 5 (2015), 427–491. Google Scholar
Digital Library
- [41] . 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 Scholar
- [42] . 2016. Convolutional matrix factorization for document context-aware recommendation. In Proceedings of the 10th ACM Conference on Recommender Systems. 233–240. Google Scholar
Digital Library
- [43] . 2009. Matrix factorization techniques for recommender systems. Computer 8 (2009), 30–37. Google Scholar
Digital Library
- [44] . 2016. When can the maximin share guarantee be guaranteed? In Proceedings of the AAAI Conference on Artificial Intelligence. AAAI, 523–529. Google Scholar
Digital Library
- [45] . 2019. Algorithmic bias? An empirical study of apparent gender-based discrimination in the display of STEM career ads. Manage. Sci. 65, 7 (2019), 2966–2981.
DOI: 10.1287/mnsc.2018.3093Google ScholarDigital Library
- [46] . 2016. Factorization meets the item embedding: Regularizing matrix factorization with item co-occurrence. In Proceedings of the 10th ACM Conference on Recommender Systems. ACM, 59–66. Google Scholar
Digital Library
- [47] . 2016. The effects of the minimum wage on earnings inequality: Evidence from China. In Income Inequality Around the World. Emerald Group Publishing Limited, 179–212.Google Scholar
- [48] . 2004. On approximately fair allocations of indivisible goods. In Proceedings of the 5th ACM Conference on Electronic Commerce (EC’04). ACM, 125–131. https://doi.org/10.1145/988772.988792 Google Scholar
Digital Library
- [49] . 2019. Retrieved from https://medium.com/public-market/in-their-own-words-why-sellers-are-fed-up-with-amazon-e97da44f7f18.Google Scholar
- [50] . 2019. Retrieved from https://www.nytimes.com/2019/09/09/business/economy/uber-lyft-california.html.Google Scholar
- [51] . 2015. A comprehensive survey of neighborhood-based recommendation methods. In Recommender Systems Handbook. Springer, 37–76.Google Scholar
Cross Ref
- [52] . 2020. FairRec: Two-sided fairness for personalized recommendations in two-sided platforms. In Proceedings of the Web Conference. 1194–1204. Google Scholar
Digital Library
- [53] . 2020. Incremental fairness in two-sided market platforms: On smoothly updating recommendations. In Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 34. 181–188.Google Scholar
Cross Ref
- [54] . 2008. A Measure of Fairness: The Economics of Living Wages and Minimum Wages in the United States. Cornell University Press.Google Scholar
Cross Ref
- [55] . 2014. Fair enough: Guaranteeing approximate maximin shares. In ACM Conference on Economics and Computation, EC. 675–692. Google Scholar
Digital Library
- [56] . 2020. Comparing fair ranking metrics. Retrieved from https://arXiv:2009.01311.Google Scholar
- [57] . 2006. Experimental study of inequality and unpredictability in an artificial cultural market. Science 311, 5762 (2006), 854–856.
DOI: DOI: https://doi.org/10.1126/science.1121066Google ScholarCross Ref
- [58] . 2017. Fairness in package-to-group recommendations. In Proceedings of the World Wide Web Conference (WWW’17).Google Scholar
Digital Library
- [59] . 2018. Fairness of exposure in rankings. In Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 2219–2228. Google Scholar
Digital Library
- [60] . 2019. Retrieved from https://slate.com/technology/2019/06/billion-dollar-bully-documentary-yelp.html.Google Scholar
- [61] . 1948. The problem of fair division. Econometrica 16 (1948), 101–104.Google Scholar
- [62] . 1980. How to cut a cake fairly. Amer. Mathe. Monthly 87, 8 (1980), 640–644.Google Scholar
Cross Ref
- [63] . 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 Scholar
Digital Library
- [64] . 2018. Multistakeholder recommendation with provider constraints. In Proceedings of the 12th ACM Conference on Recommender Systems. 54–62. Google Scholar
Digital Library
- [65] . 1974. Equity, envy, and efficiency. J. Econ. Theory 9, 1 (1974), 63–91.Google Scholar
Cross Ref
- [66] . 2019. Retrieved from https://www.wired.com/story/europes-new-rules-aim-make-online-marketplaces-more-fair/.Google Scholar
- [67] . 2017. Deep matrix factorization models for recommender systems. In Proceedings of the International Joint Conference on Artificial Intelligence (IJCAI’17), Vol. 17. 3203–3209. Google Scholar
Digital Library
- [68] . 2011. Functional matrix factorizations for cold-start recommendation. In Proceedings of the 34th International ACM SIGIR Conference on Research and Development in Information Retrieval. 315–324. Google Scholar
Digital Library
Index Terms
Toward Fair Recommendation in Two-sided Platforms
Recommendations
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