TFROM: A Two-sided Fairness-Aware Recommendation Model for Both
  Customers and Providers
  At present, most research on the fairness of recommender systems is conducted
either from the perspective of customers or from the perspective of product (or
service) providers. However, such a practice ignores the fact that when
fairness is guaranteed to one side, the fairness and rights of the other side
are likely to reduce. In this paper, we consider recommendation scenarios from
the perspective of two sides (customers and providers). From the perspective of
providers, we consider the fairness of the providers' exposure in recommender
system. For customers, we consider the fairness of the reduced quality of
recommendation results due to the introduction of fairness measures. We
theoretically analyzed the relationship between recommendation quality,
customers fairness, and provider fairness, and design a two-sided
fairness-aware recommendation model (TFROM) for both customers and providers.
Specifically, we design two versions of TFROM for offline and online
recommendation. The effectiveness of the model is verified on three real-world
data sets. The experimental results show that TFROM provides better two-sided
fairness while still maintaining a higher level of personalization than the
baseline algorithms.
