A Survey on the Fairness of Recommender Systems (2206.03761v2)

Abstract: Recommender systems are an essential tool to relieve the information overload challenge and play an important role in people's daily lives. Since recommendations involve allocations of social resources (e.g., job recommendation), an important issue is whether recommendations are fair. Unfair recommendations are not only unethical but also harm the long-term interests of the recommender system itself. As a result, fairness issues in recommender systems have recently attracted increasing attention. However, due to multiple complex resource allocation processes and various fairness definitions, the research on fairness in recommendation is scattered. To fill this gap, we review over 60 papers published in top conferences/journals, including TOIS, SIGIR, and WWW. First, we summarize fairness definitions in the recommendation and provide several views to classify fairness issues. Then, we review recommendation datasets and measurements in fairness studies and provide an elaborate taxonomy of fairness methods in the recommendation. Finally, we conclude this survey by outlining some promising future directions.

An Analytical Overview of "A Survey on the Fairness of Recommender Systems"

The paper "A Survey on the Fairness of Recommender Systems" by Wang et al. offers a comprehensive examination of the burgeoning field of fairness in recommender systems. As recommender systems increasingly mediate access to diverse resources, ensuring fair resource allocation becomes imperative due to the potential for these systems to manifest and perpetuate societal biases. This survey paper meticulously synthesizes over 60 publications from top academic forums, thereby providing a foundational reference for researchers aiming to navigate this multifaceted research area.

Fairness Definitions and Classification in Recommender Systems

The paper categorizes fairness in recommender systems into multiple dimensions. Primarily, fairness considerations are grouped into two categories—process fairness and outcome fairness. Process fairness, akin to procedural justice, emphasizes the fairness of the processes involved in generating recommendations. Conversely, outcome fairness focuses on the equity of the recommendations themselves, aiming for distributive justice.

Within outcome fairness, the authors distinguish between individual and group fairness, with further subdivisions based on fairness concepts such as consistent fairness, calibrated fairness, envy-free fairness, counterfactual fairness, Rawlsian maximin fairness, and maximin-shared fairness. Each of these concepts requires specific criteria or metrics to assess fairness. Recognizing the complexity of the fairness ecosystem, the paper highlights that group fairness has seen more extensive investigation compared to individual fairness.

Methodological Approaches to Fairness

Wang et al. classify methods for achieving fairness into three overarching categories: data-oriented methods, ranking methods, and re-ranking methods. Data-oriented approaches manipulate training data to preemptively address biases. Ranking methods, including regularization and adversarial learning, adjust recommendation algorithms or objectives to ensure fairness directly during model training. Given the goal of long-term fairness, approaches such as reinforcement learning are explored within this context. Re-ranking methods adjust outputs post hoc to balance fairness and utility, with variants focusing on slot-wise, user-wise, and global-wise re-ranking.

Fairness Metrics and Evaluation

In evaluating fairness, the paper elaborates on several metrics applicable to both individual and group settings. These metrics include measures such as absolute difference, variance, and Gini coefficients for consistent fairness, and KL-divergence and L1-norm for calibrated fairness. The paper of these metrics highlights the nuanced considerations necessary when selecting appropriate fairness evaluations for different contexts and stakeholder requirements.

Theoretical and Practical Implications

The survey dives into the implications of ensuring fairness in recommender systems, underscoring both theoretical and practical considerations. Theoretically, the balance between fairness and accuracy is of paramount importance, and the lack of consensus on universal definitions indicates a space for ongoing research. Practically, the paper identifies gaps in current benchmarks, emphasizing the need for comprehensive datasets that incorporate fairness-related attributes across diverse domains, including newer modalities such as short videos.

Future Directions

The authors offer several future directions, including the exploration of fairness definitions beyond classical boundaries, the development of benchmarks for fair comparison, and the design of algorithms achieving fairness without sacrificing accuracy. Additionally, they advocate for the integration of causal inference to tackle the root causes of algorithmic unfairness, which may facilitate more precise interventions and metric designs.

Conclusion

Wang et al. have curated an insightful survey that serves not only as a roadmap for current research but also as a call to action for addressing fairness in recommender systems. As this domain continues to evolve, it will be critical for researchers to balance technical advancements with ethical considerations, ensuring that recommender systems contribute positively to society. This survey thus lays the groundwork for future explorations that can potentially reshape how fairness is conceived and operationalized in algorithmic recommendations.