Beyond Parity: Fairness Objectives for Collaborative Filtering (1705.08804v2)

Abstract: We study fairness in collaborative-filtering recommender systems, which are sensitive to discrimination that exists in historical data. Biased data can lead collaborative-filtering methods to make unfair predictions for users from minority groups. We identify the insufficiency of existing fairness metrics and propose four new metrics that address different forms of unfairness. These fairness metrics can be optimized by adding fairness terms to the learning objective. Experiments on synthetic and real data show that our new metrics can better measure fairness than the baseline, and that the fairness objectives effectively help reduce unfairness.

Fairness Objectives for Collaborative Filtering in Recommender Systems

The paper "Beyond Parity: Fairness Objectives for Collaborative Filtering" by Sirui Yao and Bert Huang addresses the critical issue of fairness in collaborative-filtering recommender systems. Traditional collaborative-filtering methods are susceptible to bias inherent in historical data, leading to unfair predictions that can disadvantage minority user groups. The authors identify the limitations of existing fairness metrics, proposing four novel metrics that address different forms of unfairness within these systems.

Core Contributions

1. Novel Fairness Metrics: The paper introduces four metrics—value unfairness, absolute unfairness, underestimation unfairness, and overestimation unfairness—each targeting specific aspects of unfairness. These metrics are tailored to reflect the complex dynamics of collaborative filtering where user preferences inferred from co-occurrence statistics can inherently propagate demographic biases.
2. Experimentation and Validation: The authors conduct experiments using both synthetic and real-world datasets to validate the efficacy of their proposed metrics. Using a matrix factorization framework, the paper show that these fairness metrics can be optimized by incorporating them into the learning objectives as regularization terms. The results indicate that it is feasible to reduce unfairness significantly without degrading predictive accuracy.
3. Addressing Biased Data: The research highlights two primary sources of bias—population imbalance and observation bias—in data used for collaborative filtering. Synthetic data experiments simulate these biases, demonstrating how they lead to unfair recommendations and exploring how the introduced metrics can mitigate these biases.

Implications and Future Directions

The research has significant practical and theoretical implications. From a practical perspective, incorporating these fairness metrics can potentially improve the quality and inclusivity of recommendations across various applications, including e-commerce and personalized content delivery. Theoretically, the metrics offer a nuanced framework for analyzing fairness in machine learning algorithms designed for personalized recommendations.

Future research could extend these metrics to address item-based fairness, ensuring fairness in how different items or content are recommended, potentially avoiding biases against underrepresented items. Moreover, examining the robustness of these metrics in sparse data settings and exploring probabilistic models that account for two-stage sampling processes (rating and observation) could offer further refinements to the framework.

In conclusion, this paper contributes meaningfully to the fairness landscape in collaborative filtering, offering insights and tools that serve as steps towards more equitable recommendation systems. The focus on distinct types of biases and the development of specific fairness metrics is a valuable asset for researchers and practitioners aiming to enhance fairness and representation in recommender systems.