Promoting Fairness in Recommender Systems: A Multifaceted Approach

Abstract

The increasing prevalence of recommender systems necessitates a critical examination of fairness concerns. Biases within data or algorithms can lead to discriminatory recommendations, hindering user experience and potentially causing societal harm. This review paper delves into the multifaceted landscape of promoting fairness in recommender systems. We explore various debiasing techniques, including data preprocessing (e.g., cleaning and filtering) to address biased data, fairness-aware data augmentation to counter representation imbalances and algorithmic debiasing approaches that promote equitable treatment across user groups. We further examine fairness aware metrics that go beyond traditional measures like click-through rates, such as parity metrics (e.g., statistical parity, equality of opportunity) and diversity metrics (e.g., coverage, novelty) to ensure fair distribution and recommendation variety for different users. Finally, we emphasize the significance of transparency and user control. By providing users with insights into recommendation rationale and empowering them to manage their data and preferences, we can build trust and foster a more equitable recommendation ecosystem. This review paper sheds light on the current state-of-the-art approaches to fair recommender systems, paving the way for future research and development in this crucial area.