Comprehensive Fair Meta-learned Recommender System
Comprehensive Fair Meta-learned Recommender System
复制标题
DOI：
10.1145/3534678.3539269
复制
发表时间：
2022-06
期刊：
影响因子：
--
通讯作者：
Tianxin Wei;Jingrui He
中科院分区：
文献类型：
--
作者：
Tianxin Wei;Jingrui He
在推荐系统中，一个常见的挑战是冷启动问题，即系统中的新用户的交互非常有限。为了应对这一挑战，最近，许多工作将元优化思想引入推荐场景，即通过仅有的少量过往交互项目来学习用户偏好。其核心思想是为所有用户学习全局共享的元初始化参数，并将它们分别快速适配为每个用户的局部参数。它们旨在从不同用户的偏好学习中获取通用知识，以便利用所学的先验知识和少量训练数据快速适应未来的新用户。然而，先前的研究表明，推荐系统通常容易受到偏差和不公平性的影响。尽管元学习在改善冷启动推荐性能方面取得了成功，但公平性问题在很大程度上被忽视了。在本文中，我们提出了一个全面的公平元学习框架，名为CLOVER，用于确保元学习推荐模型的公平性。我们系统地研究了推荐系统中的三种公平性——个体公平性、反事实公平性和群体公平性，并提出通过多任务对抗学习方案来满足这三种公平性。我们的框架提供了一种通用的训练范式，适用于不同的元学习推荐系统。我们在三个真实世界数据集上的代表性元学习用户偏好估计器上展示了CLOVER的有效性。实验结果表明，CLOVER在不降低整体冷启动推荐性能的情况下实现了全面的公平性。
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.