The issue of fairness in recommendation is becoming increasingly essential as
Recommender Systems touch and influence more and more people in their daily
lives. In fairness-aware recommendation, most of the existing algorithmic
approaches mainly aim at solving a constrained optimization problem by imposing
a constraint on the level of fairness while optimizing the main recommendation
objective, e.g., CTR. While this alleviates the impact of unfair
recommendations, the expected return of an approach may significantly
compromise the recommendation accuracy due to the inherent trade-off between
fairness and utility. This motivates us to deal with these conflicting
objectives and explore the optimal trade-off between them in recommendation.
One conspicuous approach is to seek a Pareto efficient solution to guarantee
optimal compromises between utility and fairness. Moreover, considering the
needs of real-world e-commerce platforms, it would be more desirable if we can
generalize the whole Pareto Frontier, so that the decision-makers can specify
any preference of one objective over another based on their current business
needs. Therefore, in this work, we propose a fairness-aware recommendation
framework using multi-objective reinforcement learning, called MoFIR, which is
able to learn a single parametric representation for optimal recommendation
policies over the space of all possible preferences. Specially, we modify
traditional DDPG by introducing conditioned network into it, which conditions
the networks directly on these preferences and outputs Q-value-vectors.
Experiments on several real-world recommendation datasets verify the
superiority of our framework on both fairness metrics and recommendation
measures when compared with all other baselines. We also extract the
approximate Pareto Frontier on real-world datasets generated by MoFIR and
compare to state-of-the-art fairness methods.

本文提出了一种基于多目标强化学习的公平感知的推荐框架（MoFIR），能够以单参数表示形式学习所有可能偏好的最优推荐策略，并在多个真实推荐数据集上的实验中验证了其在公平指标和推荐度量方面的优越性。