A Personalized Framework for Consumer and Producer Group Fairness Optimization in Recommender Systems

TL;DR

This paper introduces CP-FairRank, a re-ranking algorithm that optimizes fairness for both consumers and producers in recommender systems, adaptable to various settings and validated on multiple datasets.

Contribution

It presents a novel joint fairness optimization framework for recommender systems that considers both user and item fairness simultaneously.

Findings

Improves consumer and producer fairness simultaneously

Maintains recommendation quality with minimal compromise

Validated on eight datasets and four recommendation models

Abstract

In recent years, there has been an increasing recognition that when machine learning (ML) algorithms are used to automate decisions, they may mistreat individuals or groups, with legal, ethical, or economic implications. Recommender systems are prominent examples of these machine learning (ML) systems that aid users in making decisions. The majority of past literature research on RS fairness treats user and item fairness concerns independently, ignoring the fact that recommender systems function in a two-sided marketplace. In this paper, we propose CP-FairRank, an optimization-based re-ranking algorithm that seamlessly integrates fairness constraints from both the consumer and producer side in a joint objective framework. The framework is generalizable and may take into account varied fairness settings based on group segmentation, recommendation model selection, and domain, which is one of its key characteristics. For instance, we demonstrate that the system may jointly increase consumer and producer fairness when (un)protected consumer groups are defined on the basis of their activity level and main-streamness, while producer groups are defined according to their popularity level. For empirical validation, through large-scale on eight datasets and four mainstream collaborative filtering (CF) recommendation models, we demonstrate that our proposed strategy is able to improve both consumer and producer fairness without compromising or very little overall recommendation quality, demonstrating the role algorithms may play in avoiding data biases.