FairRec#

Note

Learning tasks: Recommendation systems.

Introduction#

FairRec is an algorithm designed to ensure two-sided fairness in personalized recommendation systems on two-sided platforms. It aims to balance the interests of both producers and customers by guaranteeing a minimum exposure for producers and ensuring that customers receive recommendations that are envy-free up to one item (EF1).

Description#

The FairRec algorithm addresses the problem of fair allocation in the context of recommendation systems. The goal is to allocate products to customers in a way that ensures fairness for both producers and customers. The main characteristics of FairRec include:

  • Producer Fairness: Ensuring that each producer receives a minimum level of exposure, which is at least their maximin share (MMS) of exposure.

  • Customer Fairness: Ensuring that the recommendations are envy-free up to one item (EF1) for all customers.

The algorithm operates in two phases:

  1. First Phase: Ensures EF1 among all customers and tries to provide a minimum guarantee on the exposure of the producers.

  2. Second Phase: Ensures that exactly k products are allocated to each customer while maintaining EF1 for customers.

Basic Usage#

You can find an example of using the FairRec method in the following demo.

Read more about the class attributes and methods in the API reference: FairRec.

References#

  1. Patro, Gourab K., et al. “Fairrec: Two-sided fairness for personalized recommendations in two-sided platforms.” Proceedings of The Web Conference 2020. 2020.