  In recommender systems, one common challenge is the cold-start problem, where
interactions are very limited for fresh users in the systems. To address this
challenge, recently, many works introduce the meta-optimization idea into the
recommendation scenarios, i.e. learning to learn the user preference by only a
few past interaction items. The core idea is to learn global shared
meta-initialization parameters for all users and rapidly adapt them into local
parameters for each user respectively. They aim at deriving general knowledge
across preference learning of various users, so as to rapidly adapt to the
future new user with the learned prior and a small amount of training data.
However, previous works have shown that recommender systems are generally
vulnerable to bias and unfairness. Despite the success of meta-learning at
improving the recommendation performance with cold-start, the fairness issues
are largely overlooked. In this paper, we propose a comprehensive fair
meta-learning framework, named CLOVER, for ensuring the fairness of
meta-learned recommendation models. We systematically study three kinds of
fairness - individual fairness, counterfactual fairness, and group fairness in
the recommender systems, and propose to satisfy all three kinds via a
multi-task adversarial learning scheme. Our framework offers a generic training
paradigm that is applicable to different meta-learned recommender systems. We
demonstrate the effectiveness of CLOVER on the representative meta-learned user
preference estimator on three real-world data sets. Empirical results show that
CLOVER achieves comprehensive fairness without deteriorating the overall
cold-start recommendation performance.
