  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.
