FAST: A Fairness Assured Service Recommendation Strategy Considering Service Capacity Constraint
This paper tackles the problem of fair service recommendations for users when services have capacity constraints, which is an incremental improvement to existing recommender systems.
This paper addresses the problem of service recommendations under capacity constraints, where too many recommendations for a popular service can degrade quality. The authors propose FAST, a strategy that adjusts recommendation lists to ensure long-term fairness in multi-round recommendations, demonstrating improved fairness on Yelp and synthetic datasets while maintaining high recommendation quality.
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.