Comprehensive Fair Meta-learned Recommender System

by   Tianxin Wei, et al.

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.


page 1

page 2

page 3

page 4


MAMO: Memory-Augmented Meta-Optimization for Cold-start Recommendation

A common challenge for most current recommender systems is the cold-star...

Task-adaptive Neural Process for User Cold-Start Recommendation

User cold-start recommendation is a long-standing challenge for recommen...

In-processing User Constrained Dominant Sets for User-Oriented Fairness in Recommender Systems

Recommender systems are typically biased toward a small group of users, ...

How to Retrain Recommender System? A Sequential Meta-Learning Method

Practical recommender systems need be periodically retrained to refresh ...

Learning to Learn a Cold-start Sequential Recommender

The cold-start recommendation is an urgent problem in contemporary onlin...

Path-Specific Counterfactual Fairness for Recommender Systems

Recommender systems (RSs) have become an indispensable part of online pl...

Forgetting Fast in Recommender Systems

Users of a recommender system may want part of their data being deleted,...

Please sign up or login with your details

Forgot password? Click here to reset