Adaptive Fair Representation Learning for Personalized Fairness in Recommendations via Information Alignment

Core Concepts

The core message of this paper is to propose a novel Adaptive Fair Representation Learning (AFRL) model that achieves personalized fairness in recommendations by treating fairness requirements as inputs and learning attribute-specific embeddings and a debiased collaborative embedding, without compromising recommendation accuracy.

Abstract

The paper proposes a novel Adaptive Fair Representation Learning (AFRL) model for personalized fairness in recommendations. The key highlights are:

AFRL treats fairness requirements as inputs rather than hyperparameters, allowing it to adaptively generate fair embeddings for different users during the inference phase. This overcomes the challenge of the unacceptable training cost incurred by the explosion of attribute combinations in existing methods.

AFRL introduces an Information Alignment Module (IAlignM) that learns attribute-specific embeddings and a debiased collaborative embedding. This allows AFRL to exactly preserve the discriminative information of non-sensitive attributes and incorporate unbiased collaborative signals, achieving a better trade-off between fairness and accuracy compared to existing approaches that remove sensitive attribute information.

The extensive experiments and theoretical analysis demonstrate the superiority of AFRL over state-of-the-art personalized fairness models in terms of both fairness and accuracy.

Stats

The dataset contains more than 1 million movie ratings provided by 6,040 users with attributes Gender, Age, and Occupation.
The Taobao dataset comprises more than 26 million interactions between 1.14 million users and 840,000 advertisements, with user attributes Gender, Age, and Consumption level.

Quotes

"To meet diverse fairness requirements, Li et al. [24] propose to train a filter for each possible combination of sensitive attributes. Wu et al. [37] propose a model PFRec, which builds a set of prompt-based bias eliminators and adapters with customized attribute-specific prompts to learn fair embeddings for different attribute combinations."
"Creager et al. [7] propose FFVAE, a disentangled representation learning model that separates representations into sensitive and non-sensitive subspaces. It addresses personalized fairness by excluding from the learned fair embeddings the relevant semantic factors corresponding to different sensitive attributes specified by user fairness requirements."

Key Insights Distilled From

Adaptive Fair Representation Learning for Personalized Fairness in Recommendations via Information Alignment

by XInyu Zhu,Li... at arxiv.org 04-12-2024

https://arxiv.org/pdf/2404.07494.pdf

Deeper Inquiries

How can AFRL's approach to personalized fairness be extended to other recommendation tasks beyond the ones explored in this paper

AFRL's approach to personalized fairness can be extended to other recommendation tasks by adapting the model architecture and training process to suit the specific requirements of different domains. For example, in e-commerce platforms, where user attributes like purchase history and browsing behavior are crucial, AFRL can be modified to incorporate these attributes into the fairness considerations. Additionally, in content recommendation systems, attributes such as genre preferences or content consumption habits can be integrated into the model to ensure personalized fairness in recommendations. By customizing the attribute-specific embeddings and debiased collaborative signals based on the unique characteristics of each recommendation task, AFRL can be applied to a wide range of domains beyond movie or ad recommendations.

What are the potential limitations or drawbacks of AFRL's reliance on attribute-specific embeddings and debiased collaborative signals, and how could these be addressed in future work

One potential limitation of AFRL's reliance on attribute-specific embeddings and debiased collaborative signals is the risk of overfitting to the training data, especially when dealing with sparse or noisy attribute information. To address this, future work could explore techniques such as regularization or data augmentation to improve the generalization capabilities of the model. Additionally, the interpretability of the attribute-specific embeddings and collaborative signals could be enhanced to provide more insights into the fairness decisions made by the model. Moreover, the scalability of AFRL to large-scale datasets and real-time recommendation systems could be a challenge that needs to be addressed in future research.

Given the importance of user privacy in recommendation systems, how could AFRL's framework be adapted to preserve user privacy while still achieving personalized fairness

To adapt AFRL's framework to preserve user privacy while achieving personalized fairness, several strategies can be employed. One approach is to incorporate privacy-preserving techniques such as differential privacy or federated learning into the model training process. By adding noise to the attribute-specific embeddings or collaborative signals, sensitive user information can be protected while still maintaining the fairness of recommendations. Another strategy is to implement user-controlled privacy settings, allowing users to specify the level of attribute exposure they are comfortable with when receiving personalized recommendations. By giving users more control over their data and privacy preferences, AFRL can strike a balance between personalized fairness and user privacy in recommendation systems.