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Fair Recommendation of Two-sided Platforms

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Arpita Biswas
Arpita Biswas

Major eCommerce and online advertising platforms (such as Amazon, Spotify) can be thought of as two-sided platforms, with customers on one side and producers on the other. Traditionally, recommendation protocols of these platforms are customer-centric---focusing on maximizing customer satisfaction by tailoring the recommendation according to the personalized preferences of individual customers. However, this may lead to unfair distribution of exposure among the producers and adversely impact their well-being. As more and more people depend on such platforms to earn a living, it is important to strike a balance between fairness among the producers and customer satisfaction. We propose to adopt the solution concepts from the fair allocation literature in order to quantify fairness/satisfaction and provide provable guarantees. In fact, the problem of two-sided fairness in recommendation can be formulated as a matroid constrained fair allocation problem. This problem naturally captures a number of other resource-allocation applications, including budgeted course allocation, graph partition, and allocation of cloud computing resources. Our main contribution is to develop polynomial-time algorithms for fair allocation of indivisible items (with formal guarantees on fairness and feasibility) in several practical scenarios which can be formulated as matroid constrained problems. In this talk, I’ll discuss the matroid constrained fair allocation problem, and show how the solutions can be applied to ensure a two-sided fair recommendation.

Education
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#GHCI19 Two-Sided Fairness Guarantees for Recommendation Protocols in B2C Ecommerce Platforms Computational Social Choice Meets Recommendation Paradigm Arpita Biswas | arpita.biswas@live.in
PAGE 2 | GRACE HOPPER CELEBRATION INDIA (GHCI) 19 Presented by AnitaB.org and Association for Computing Machinery India (ACM) India #GHCI19 Talk Outline Recommendation System Fair Allocation of Indivisible Goods Fair Allocation Under Cardinality Constraint Two-Sided Fair Recommendation Theoretical and Experimental Results
PAGE 3 | GRACE HOPPER CELEBRATION INDIA (GHCI) 19 Presented by AnitaB.org and Association for Computing Machinery India (ACM) India #GHCI19 Recommendation System • Algorithms that suggest relevant items to users — Movies to watch — News articles to read — Products to buy • An efficient recommendation system is the most critical component of most of the e-commerce and online advertisement platforms.
PAGE 4 | GRACE HOPPER CELEBRATION INDIA (GHCI) 19 Presented by AnitaB.org and Association for Computing Machinery India (ACM) India #GHCI19 Recommendation System Post-processing Recommendation Algorithm
PAGE 5 | GRACE HOPPER CELEBRATION INDIA (GHCI) 19 Presented by AnitaB.org and Association for Computing Machinery India (ACM) India #GHCI19 Recommendation System Post-processing Image Courtesy: https://i.pinimg.com/originals/2e/b0/71/2eb071631c5fa6c710bc7f17408dd290.png Learning Relevance scores: 1. Content-Based recommendations 2. Collaborative recommendations 3. Hybrid Approaches Relevance Scores
PAGE 6 | GRACE HOPPER CELEBRATION INDIA (GHCI) 19 Presented by AnitaB.org and Association for Computing Machinery India (ACM) India #GHCI19 Recommendation System Post-processing Image Courtesy: https://i.pinimg.com/originals/2e/b0/71/2eb071631c5fa6c710bc7f17408dd290.png Popular post- processing technique Top-k recommendation Relevance Scores
PAGE 7 | GRACE HOPPER CELEBRATION INDIA (GHCI) 19 Presented by AnitaB.org and Association for Computing Machinery India (ACM) India #GHCI19 Recommendation System in B2C Ecommerce Multiple Stakeholders • Producers — of goods and services (e.g. restaurants on Google Local, hosts onAirbnb) • Customers — users of the platform , who consume the services • Platform — which sits at the center of the recommendation ecosystem, essentially accessing the information from the user and suggesting services
PAGE 8 | GRACE HOPPER CELEBRATION INDIA (GHCI) 19 Presented by AnitaB.org and Association for Computing Machinery India (ACM) India #GHCI19 Recommendation System Post-processing Image Courtesy: https://i.pinimg.com/originals/2e/b0/71/2eb071631c5fa6c710bc7f17408dd290.png Popular post- processing technique Top-k recommendation Relevance Scores
PAGE 9 | GRACE HOPPER CELEBRATION INDIA (GHCI) 19 Presented by AnitaB.org and Association for Computing Machinery India (ACM) India #GHCI19 Computational Social Choice Meets Recommendation Paradigm A novel approach towards ensuring fairness among all individuals in a two-sided market
PAGE 10 | GRACE HOPPER CELEBRATION INDIA (GHCI) 19 Presented by AnitaB.org and Association for Computing Machinery India (ACM) India #GHCI19 Recommendation Algorithm Fair Allocation of Indivisible Goods Data Fair Recommendation Computational Social Choice Meets Recommendation Paradigm Relevance scores
PAGE 11 | GRACE HOPPER CELEBRATION INDIA (GHCI) 19 Presented by AnitaB.org and Association for Computing Machinery India (ACM) India #GHCI19 Computational Social Choice Meets Recommendation Paradigm Recommendation Algorithm Fair Allocation of Indivisible Goods Data Fair Recommendation Computational Social ChoiceTheory Relevance scores
PAGE 12 | GRACE HOPPER CELEBRATION INDIA (GHCI) 19 Presented by AnitaB.org and Association for Computing Machinery India (ACM) India #GHCI19 Fair Allocation of Indivisible Goods
PAGE 13 | GRACE HOPPER CELEBRATION INDIA (GHCI) 19 Presented by AnitaB.org and Association for Computing Machinery India (ACM) India #GHCI19 Fair Allocation of Indivisible Goods
PAGE 14 | GRACE HOPPER CELEBRATION INDIA (GHCI) 19 Presented by AnitaB.org and Association for Computing Machinery India (ACM) India #GHCI19 Fairness Notions • DC Foley (1967). Resource allocation in the public sector. Yale Economic Essays, 7:73–76. • Hal R Varian (1974). Equity, Envy, and Efficiency. Journal of Economic Theory, 9(1):63–91. • Walter Stromquist (1980). How to Cut a Cake Fairly. The American Mathematical Monthly, 87(8):640–644. • Steinhaus, H. (1948). The problem of fair division. Econometrica, 16:101–104.
PAGE 15 | GRACE HOPPER CELEBRATION INDIA (GHCI) 19 Presented by AnitaB.org and Association for Computing Machinery India (ACM) India #GHCI19 Fairness Notions
PAGE 16 | GRACE HOPPER CELEBRATION INDIA (GHCI) 19 Presented by AnitaB.org and Association for Computing Machinery India (ACM) India #GHCI19 Fairness Notions
PAGE 17 | GRACE HOPPER CELEBRATION INDIA (GHCI) 19 Presented by AnitaB.org and Association for Computing Machinery India (ACM) India #GHCI19 Fairness Notions
PAGE 18 | GRACE HOPPER CELEBRATION INDIA (GHCI) 19 Presented by AnitaB.org and Association for Computing Machinery India (ACM) India #GHCI19 Envy-Freeness up to One Good (EF1)
PAGE 19 | GRACE HOPPER CELEBRATION INDIA (GHCI) 19 Presented by AnitaB.org and Association for Computing Machinery India (ACM) India #GHCI19 Envy-Freeness up to One Good (EF1) RelatedWork • R. Lipton, E. Markakis, E. Mossel, and A. Saberi (2004). On Approximately Fair Allocations of Indivisible Goods. EC, pages 125-131. • I. Caragiannis, D. Kurokawa, H. Moulin, A. Procaccia, N. Shah, and J. Wang (2016). The Unreasonable Fairness of Maximum Nash Welfare. EC, pages 305–322.
PAGE 20 | GRACE HOPPER CELEBRATION INDIA (GHCI) 19 Presented by AnitaB.org and Association for Computing Machinery India (ACM) India #GHCI19 MaxiMin Share Fairness (MMS)
PAGE 21 | GRACE HOPPER CELEBRATION INDIA (GHCI) 19 Presented by AnitaB.org and Association for Computing Machinery India (ACM) India #GHCI19 MaxiMin Share Fairness (MMS)
PAGE 22 | GRACE HOPPER CELEBRATION INDIA (GHCI) 19 Presented by AnitaB.org and Association for Computing Machinery India (ACM) India #GHCI19 Maximin ShareThreshold MaxiMin Share Fairness (MMS)
PAGE 23 | GRACE HOPPER CELEBRATION INDIA (GHCI) 19 Presented by AnitaB.org and Association for Computing Machinery India (ACM) India #GHCI19 Maximin ShareThreshold MaxiMin Share Fairness (MMS)
PAGE 24 | GRACE HOPPER CELEBRATION INDIA (GHCI) 19 Presented by AnitaB.org and Association for Computing Machinery India (ACM) India #GHCI19 MaxiMin Share Fairness (MMS) RelatedWork
PAGE 25 | GRACE HOPPER CELEBRATION INDIA (GHCI) 19 Presented by AnitaB.org and Association for Computing Machinery India (ACM) India #GHCI19 Fair Allocation under Cardinality Constraint
PAGE 26 | GRACE HOPPER CELEBRATION INDIA (GHCI) 19 Presented by AnitaB.org and Association for Computing Machinery India (ACM) India #GHCI19 Fair Allocation under Structured Set Constraints Existence and Algorithmic Results for:
PAGE 27 | GRACE HOPPER CELEBRATION INDIA (GHCI) 19 Presented by AnitaB.org and Association for Computing Machinery India (ACM) India #GHCI19 Computational Social Choice Meets Recommendation Paradigm Recommendation Algorithm Fair Allocation of Indivisible Goods Data Fair Recommendation Computational Social ChoiceTheory Relevance scores are the valuations
PAGE 28 | GRACE HOPPER CELEBRATION INDIA (GHCI) 19 Presented by AnitaB.org and Association for Computing Machinery India (ACM) India #GHCI19 Two-Sided Fairness for Recommendation Protocols Customers Producers Burgers Image Courtesy: Google Images
PAGE 29 | GRACE HOPPER CELEBRATION INDIA (GHCI) 19 Presented by AnitaB.org and Association for Computing Machinery India (ACM) India #GHCI19 Two-Sided Fairness This work is done in collaboration with IIT Kharagpur (Gourab K. Patro and Prof. Niloy Ganguly) and Max Planck Institute for Software Systems, Germany (Abhijnan Chakrab0rty and Prof. Krishna P. Gummadi) Experimental Results on Google Local Ratings Dataset (GL): Customer-side (un)fairness measure 11172 customers, 855 businesses, and 25686 reviews
PAGE 30 | GRACE HOPPER CELEBRATION INDIA (GHCI) 19 Presented by AnitaB.org and Association for Computing Machinery India (ACM) India #GHCI19 Two-Sided Fairness This work is done in collaboration with IIT Kharagpur (Gourab K. Patro and Prof. Niloy Ganguly) and Max Planck Institute for Software Systems, Germany (Abhijnan Chakrab0rty and Prof. Krishna P. Gummadi) Experimental Results on Google Local Ratings Dataset (GL): Producer-side fairness measureCustomer-side (un)fairness measure 11172 customers, 855 businesses, and 25686 reviews
PAGE 31 | GRACE HOPPER CELEBRATION INDIA (GHCI) 19 Presented by AnitaB.org and Association for Computing Machinery India (ACM) India #GHCI19 References • Fair Allocation: — Matroid Constrained Fair Allocation Problem. Biswas and Barman AAAI’19 — Fair Allocation under Cardinality Constraint. Biswas and Barman IJCAI’18 — Groupwise Maximin Share Fair Allocation of Indivisible Goods. Barman et al. AAAI’18 • Fair Classification: — Fairness Through the Lens of Proportional Equality. Biswas and Mukherjee AAMAS’19 — Ensuring Fair Predictions under Prior Probability Shifts. Joint work with Suvam Mukherjee* — Quantifying Infra-marginality and Its Trade-Offs with Group Fairness. Joint work with Siddharth Barman, Amit Deshpande, Amit Sharma* — Fairness Guarantees in Mental Health. In collaboration with Microsoft Research Cambridge* * work in progress
Thank you
PAGE 33 | GRACE HOPPER CELEBRATION INDIA (GHCI) 19 Presented by AnitaB.org and Association for Computing Machinery India (ACM) India #GHCI19 Fairness in Recommendation Related Work: • Customer-based fairness • Producer-based fairness • Two-sided group fairness

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Fair Recommendation of Two-sided Platforms

  • 1. #GHCI19 Two-Sided Fairness Guarantees for Recommendation Protocols in B2C Ecommerce Platforms Computational Social Choice Meets Recommendation Paradigm Arpita Biswas | arpita.biswas@live.in
  • 2. PAGE 2 | GRACE HOPPER CELEBRATION INDIA (GHCI) 19 Presented by AnitaB.org and Association for Computing Machinery India (ACM) India #GHCI19 Talk Outline Recommendation System Fair Allocation of Indivisible Goods Fair Allocation Under Cardinality Constraint Two-Sided Fair Recommendation Theoretical and Experimental Results
  • 3. PAGE 3 | GRACE HOPPER CELEBRATION INDIA (GHCI) 19 Presented by AnitaB.org and Association for Computing Machinery India (ACM) India #GHCI19 Recommendation System • Algorithms that suggest relevant items to users — Movies to watch — News articles to read — Products to buy • An efficient recommendation system is the most critical component of most of the e-commerce and online advertisement platforms.
  • 4. PAGE 4 | GRACE HOPPER CELEBRATION INDIA (GHCI) 19 Presented by AnitaB.org and Association for Computing Machinery India (ACM) India #GHCI19 Recommendation System Post-processing Recommendation Algorithm
  • 5. PAGE 5 | GRACE HOPPER CELEBRATION INDIA (GHCI) 19 Presented by AnitaB.org and Association for Computing Machinery India (ACM) India #GHCI19 Recommendation System Post-processing Image Courtesy: https://i.pinimg.com/originals/2e/b0/71/2eb071631c5fa6c710bc7f17408dd290.png Learning Relevance scores: 1. Content-Based recommendations 2. Collaborative recommendations 3. Hybrid Approaches Relevance Scores
  • 6. PAGE 6 | GRACE HOPPER CELEBRATION INDIA (GHCI) 19 Presented by AnitaB.org and Association for Computing Machinery India (ACM) India #GHCI19 Recommendation System Post-processing Image Courtesy: https://i.pinimg.com/originals/2e/b0/71/2eb071631c5fa6c710bc7f17408dd290.png Popular post- processing technique Top-k recommendation Relevance Scores
  • 7. PAGE 7 | GRACE HOPPER CELEBRATION INDIA (GHCI) 19 Presented by AnitaB.org and Association for Computing Machinery India (ACM) India #GHCI19 Recommendation System in B2C Ecommerce Multiple Stakeholders • Producers — of goods and services (e.g. restaurants on Google Local, hosts onAirbnb) • Customers — users of the platform , who consume the services • Platform — which sits at the center of the recommendation ecosystem, essentially accessing the information from the user and suggesting services
  • 8. PAGE 8 | GRACE HOPPER CELEBRATION INDIA (GHCI) 19 Presented by AnitaB.org and Association for Computing Machinery India (ACM) India #GHCI19 Recommendation System Post-processing Image Courtesy: https://i.pinimg.com/originals/2e/b0/71/2eb071631c5fa6c710bc7f17408dd290.png Popular post- processing technique Top-k recommendation Relevance Scores
  • 9. PAGE 9 | GRACE HOPPER CELEBRATION INDIA (GHCI) 19 Presented by AnitaB.org and Association for Computing Machinery India (ACM) India #GHCI19 Computational Social Choice Meets Recommendation Paradigm A novel approach towards ensuring fairness among all individuals in a two-sided market
  • 10. PAGE 10 | GRACE HOPPER CELEBRATION INDIA (GHCI) 19 Presented by AnitaB.org and Association for Computing Machinery India (ACM) India #GHCI19 Recommendation Algorithm Fair Allocation of Indivisible Goods Data Fair Recommendation Computational Social Choice Meets Recommendation Paradigm Relevance scores
  • 11. PAGE 11 | GRACE HOPPER CELEBRATION INDIA (GHCI) 19 Presented by AnitaB.org and Association for Computing Machinery India (ACM) India #GHCI19 Computational Social Choice Meets Recommendation Paradigm Recommendation Algorithm Fair Allocation of Indivisible Goods Data Fair Recommendation Computational Social ChoiceTheory Relevance scores
  • 12. PAGE 12 | GRACE HOPPER CELEBRATION INDIA (GHCI) 19 Presented by AnitaB.org and Association for Computing Machinery India (ACM) India #GHCI19 Fair Allocation of Indivisible Goods
  • 13. PAGE 13 | GRACE HOPPER CELEBRATION INDIA (GHCI) 19 Presented by AnitaB.org and Association for Computing Machinery India (ACM) India #GHCI19 Fair Allocation of Indivisible Goods
  • 14. PAGE 14 | GRACE HOPPER CELEBRATION INDIA (GHCI) 19 Presented by AnitaB.org and Association for Computing Machinery India (ACM) India #GHCI19 Fairness Notions • DC Foley (1967). Resource allocation in the public sector. Yale Economic Essays, 7:73–76. • Hal R Varian (1974). Equity, Envy, and Efficiency. Journal of Economic Theory, 9(1):63–91. • Walter Stromquist (1980). How to Cut a Cake Fairly. The American Mathematical Monthly, 87(8):640–644. • Steinhaus, H. (1948). The problem of fair division. Econometrica, 16:101–104.
  • 15. PAGE 15 | GRACE HOPPER CELEBRATION INDIA (GHCI) 19 Presented by AnitaB.org and Association for Computing Machinery India (ACM) India #GHCI19 Fairness Notions
  • 16. PAGE 16 | GRACE HOPPER CELEBRATION INDIA (GHCI) 19 Presented by AnitaB.org and Association for Computing Machinery India (ACM) India #GHCI19 Fairness Notions
  • 17. PAGE 17 | GRACE HOPPER CELEBRATION INDIA (GHCI) 19 Presented by AnitaB.org and Association for Computing Machinery India (ACM) India #GHCI19 Fairness Notions
  • 18. PAGE 18 | GRACE HOPPER CELEBRATION INDIA (GHCI) 19 Presented by AnitaB.org and Association for Computing Machinery India (ACM) India #GHCI19 Envy-Freeness up to One Good (EF1)
  • 19. PAGE 19 | GRACE HOPPER CELEBRATION INDIA (GHCI) 19 Presented by AnitaB.org and Association for Computing Machinery India (ACM) India #GHCI19 Envy-Freeness up to One Good (EF1) RelatedWork • R. Lipton, E. Markakis, E. Mossel, and A. Saberi (2004). On Approximately Fair Allocations of Indivisible Goods. EC, pages 125-131. • I. Caragiannis, D. Kurokawa, H. Moulin, A. Procaccia, N. Shah, and J. Wang (2016). The Unreasonable Fairness of Maximum Nash Welfare. EC, pages 305–322.
  • 20. PAGE 20 | GRACE HOPPER CELEBRATION INDIA (GHCI) 19 Presented by AnitaB.org and Association for Computing Machinery India (ACM) India #GHCI19 MaxiMin Share Fairness (MMS)
  • 21. PAGE 21 | GRACE HOPPER CELEBRATION INDIA (GHCI) 19 Presented by AnitaB.org and Association for Computing Machinery India (ACM) India #GHCI19 MaxiMin Share Fairness (MMS)
  • 22. PAGE 22 | GRACE HOPPER CELEBRATION INDIA (GHCI) 19 Presented by AnitaB.org and Association for Computing Machinery India (ACM) India #GHCI19 Maximin ShareThreshold MaxiMin Share Fairness (MMS)
  • 23. PAGE 23 | GRACE HOPPER CELEBRATION INDIA (GHCI) 19 Presented by AnitaB.org and Association for Computing Machinery India (ACM) India #GHCI19 Maximin ShareThreshold MaxiMin Share Fairness (MMS)
  • 24. PAGE 24 | GRACE HOPPER CELEBRATION INDIA (GHCI) 19 Presented by AnitaB.org and Association for Computing Machinery India (ACM) India #GHCI19 MaxiMin Share Fairness (MMS) RelatedWork
  • 25. PAGE 25 | GRACE HOPPER CELEBRATION INDIA (GHCI) 19 Presented by AnitaB.org and Association for Computing Machinery India (ACM) India #GHCI19 Fair Allocation under Cardinality Constraint
  • 26. PAGE 26 | GRACE HOPPER CELEBRATION INDIA (GHCI) 19 Presented by AnitaB.org and Association for Computing Machinery India (ACM) India #GHCI19 Fair Allocation under Structured Set Constraints Existence and Algorithmic Results for:
  • 27. PAGE 27 | GRACE HOPPER CELEBRATION INDIA (GHCI) 19 Presented by AnitaB.org and Association for Computing Machinery India (ACM) India #GHCI19 Computational Social Choice Meets Recommendation Paradigm Recommendation Algorithm Fair Allocation of Indivisible Goods Data Fair Recommendation Computational Social ChoiceTheory Relevance scores are the valuations
  • 28. PAGE 28 | GRACE HOPPER CELEBRATION INDIA (GHCI) 19 Presented by AnitaB.org and Association for Computing Machinery India (ACM) India #GHCI19 Two-Sided Fairness for Recommendation Protocols Customers Producers Burgers Image Courtesy: Google Images
  • 29. PAGE 29 | GRACE HOPPER CELEBRATION INDIA (GHCI) 19 Presented by AnitaB.org and Association for Computing Machinery India (ACM) India #GHCI19 Two-Sided Fairness This work is done in collaboration with IIT Kharagpur (Gourab K. Patro and Prof. Niloy Ganguly) and Max Planck Institute for Software Systems, Germany (Abhijnan Chakrab0rty and Prof. Krishna P. Gummadi) Experimental Results on Google Local Ratings Dataset (GL): Customer-side (un)fairness measure 11172 customers, 855 businesses, and 25686 reviews
  • 30. PAGE 30 | GRACE HOPPER CELEBRATION INDIA (GHCI) 19 Presented by AnitaB.org and Association for Computing Machinery India (ACM) India #GHCI19 Two-Sided Fairness This work is done in collaboration with IIT Kharagpur (Gourab K. Patro and Prof. Niloy Ganguly) and Max Planck Institute for Software Systems, Germany (Abhijnan Chakrab0rty and Prof. Krishna P. Gummadi) Experimental Results on Google Local Ratings Dataset (GL): Producer-side fairness measureCustomer-side (un)fairness measure 11172 customers, 855 businesses, and 25686 reviews
  • 31. PAGE 31 | GRACE HOPPER CELEBRATION INDIA (GHCI) 19 Presented by AnitaB.org and Association for Computing Machinery India (ACM) India #GHCI19 References • Fair Allocation: — Matroid Constrained Fair Allocation Problem. Biswas and Barman AAAI’19 — Fair Allocation under Cardinality Constraint. Biswas and Barman IJCAI’18 — Groupwise Maximin Share Fair Allocation of Indivisible Goods. Barman et al. AAAI’18 • Fair Classification: — Fairness Through the Lens of Proportional Equality. Biswas and Mukherjee AAMAS’19 — Ensuring Fair Predictions under Prior Probability Shifts. Joint work with Suvam Mukherjee* — Quantifying Infra-marginality and Its Trade-Offs with Group Fairness. Joint work with Siddharth Barman, Amit Deshpande, Amit Sharma* — Fairness Guarantees in Mental Health. In collaboration with Microsoft Research Cambridge* * work in progress
  • 33. PAGE 33 | GRACE HOPPER CELEBRATION INDIA (GHCI) 19 Presented by AnitaB.org and Association for Computing Machinery India (ACM) India #GHCI19 Fairness in Recommendation Related Work: • Customer-based fairness • Producer-based fairness • Two-sided group fairness

Editor's Notes

  1. Good morning everyone. It is really an honor for me to share my research at the Grace Hopper Celebration. This event is very close to my heart and I am thankful to Anita Borg organization and ACM India for organizing this wonderful event. I am Arpita, I am a Google PhD Fellow at the Indian Institute of Science. My research lies in the intersection of algorithmic game theory and machine learning. Today I’m going to speak about two-sided fairness guarantees for recommendation protocols in B2C ecommerce platforms.
  2. Here’s the outline of my talk. I’ll start by describing what is a recommendation system. Then, I’ll spend some time describing the problem of fair allocation of indivisible goods and its constrained settings. Then I’ll show how one can leverage the fair allocation problem to solve fairness in recommendation systems. I’ll conclude by listing some of the exciting results that we have so far.
  3. In a very general way, recommender systems are algorithms aimed at suggesting relevant items to users (items being movies to watch, news articles to read, products to buy, etc.). Nowadays, recommender systems are unavoidable in our daily online journeys, mainly because of the rise of online platforms such as Amazon, Netflix, YouTube, and food delivery apps. An efficient recommendation system is the most critical component of several e-commerce (suggest to buyers articles that could interest them) to online advertisement (suggest to users the right contents, matching their preferences), a
  4. Let us look at a scenario to understand the recommendation process. To create this personalized playlist for the user, the online music service runs a recommendation algorithm
  5. This task typically requires learning the relevance scoring functions.
  6. Traditionally, the goal of personalized recommendations have been to recommend products that would be most relevant to a customer. Once the relevance scores are predicted, the standard practice across several recommender systems is to recommend the top-k relevant items. While this top-k approach maximizes the satisfaction of individual customers, it may not be the best post-processing technique when it comes to B2C e-commerce platforms where the platforms need to balance producer satisfaction as well as customer satisfaction.
  7. There are multiple stakeholders in these platforms: (i) producers of goods and services (e.g.,restaurants on Google Local, hosts on Airbnb), (ii) customers who consume them, and (iii) the platform which sits at the center of the ecosystem, essentially controlling the information access through recommendation services.
  8. New songs/artists will never be recommended.
  9. We propose…
  10. Recall that the post-processing is an important step in the recommendation system workflow. Our contribution is to consider this post-processing step as being a solution to the problem of fair allocation of indivisible goods.
  11. This problem stems from the theory of computational social choice where the goal is to find solutions that aim to provide welfare of individuals instead of solely maximizing revenue.
  12. This problem find its application in several real-world…