FAST: A Fairness Assured Service Recommendation Strategy Considering Service Capacity Constraint

TL;DR

This paper introduces FAST, a novel recommendation strategy that ensures fairness over multiple rounds while respecting service capacity constraints, improving fairness without sacrificing recommendation quality.

Contribution

The paper proposes Top-N Fairness as a new metric and develops FAST, a strategy that guarantees long-term fairness in service recommendations considering capacity limits.

Findings

FAST achieves better fairness in recommendations.

FAST maintains high recommendation quality.

Theoretical proof of convergence for fairness variance.

Abstract

An excessive number of customers often leads to a degradation in service quality. However, the capacity constraints of services are ignored by recommender systems, which may lead to unsatisfactory recommendation. This problem can be solved by limiting the number of users who receive the recommendation for a service, but this may be viewed as unfair. In this paper, we propose a novel metric Top-N Fairness to measure the individual fairness of multi-round recommendations of services with capacity constraints. By considering the fact that users are often only affected by top-ranked items in a recommendation, Top-N Fairness only considers a sub-list consisting of top N services. Based on the metric, we design FAST, a Fairness Assured service recommendation STrategy. FAST adjusts the original recommendation list to provide users with recommendation results that guarantee the long-term fairness of multi-round recommendations. We prove the convergence property of the variance of Top-N Fairness of FAST theoretically. FAST is tested on the Yelp dataset and synthetic datasets. The experimental results show that FAST achieves better recommendation fairness while still maintaining high recommendation quality.