FairRec: Two-Sided Fairness for Personalized Recommendations in Two-Sided Platforms

Abstract: We investigate the problem of fair recommendation in the context of two-sided online platforms, comprising customers on one side and producers on the other. Traditionally, recommendation services in these platforms have focused on maximizing customer satisfaction by tailoring the results according to the personalized preferences of individual customers. However, our investigation reveals that such customer-centric design may lead to unfair distribution of exposure among the producers, which may adversely impact their well-being. On the other hand, a producer-centric design might become unfair to the customers. Thus, we consider fairness issues that span both customers and producers. Our approach involves a novel mapping of the fair recommendation problem to a constrained version of the problem of fairly allocating indivisible goods. Our proposed FairRec algorithm guarantees at least Maximin Share (MMS) of exposure for most of the producers and Envy-Free up to One item (EF1) fairness for every customer. Extensive evaluations over multiple real-world datasets show the effectiveness of FairRec in ensuring two-sided fairness while incurring a marginal loss in the overall recommendation quality.

• The paper introduces FairRec, an algorithm that models personalized recommendations as a fair allocation problem balancing producer and consumer benefits.
• It guarantees envy-free up to one item (EF1) for consumers and maximin share (MMS) exposure for most producers using principles from social choice theory.
• Experimental evaluations show FairRec minimizes exposure disparities while maintaining competitive recommendation quality across real-world datasets.

FairRec: Two-Sided Fairness for Personalized Recommendations in Two-Sided Platforms

The paper "FairRec: Two-Sided Fairness for Personalized Recommendations in Two-Sided Platforms" addresses the crucial issue of fairness in recommendation systems deployed on two-sided platforms. These platforms, essential in the digital economy, include entities like Netflix, Amazon, and Spotify, which bridge the gap between producers (e.g., artists, merchants) and consumers (e.g., viewers, shoppers). The central problem investigated is the imbalance of exposure benefits, which traditionally skew heavily towards consumer satisfaction, potentially at the expense of producers.

Problem Statement

Most recommendation systems prioritize consumer satisfaction by optimizing for personalized preferences, often leading to unequal exposure distribution among producers. This can negatively affect producers who rely on these platforms for visibility and sales. Conversely, focusing too much on producer exposure could detract from consumer satisfaction. The paper explores this trade-off and attempts to design a balanced approach that offers fair recommendations for both parties.

Methodology

The authors propose the FairRec algorithm, which conceptualizes recommendation as a fair allocation problem. This involves adapting principles from social choice theory, notably envy-free up to one item (EF1) for consumers and ensuring a Maximin Share (MMS) exposure guarantee for most producers. The algorithm, notable for its practicality and scalability, performs this allocation by initially placing the problem within the framework of allocating indivisible resources fairly. This involves:

• Ensuring each producer receives at least their maximin exposure share.
• Ensuring that the recommendations are envy-free up to one item, thus preventing significant disparities in consumer utility.

Theoretical Contributions

The paper provides formal proofs that FairRec guarantees EF1 fairness among consumers while optimizing for producer exposure fairness. The algorithm efficiently ensures that all producers receive some exposure, with most satisfying their MMS of exposure. These assurances position FairRec as an appealing solution in domains where both consumer satisfaction and producer welfare are paramount.

Experimental Evaluation

The authors conducted extensive evaluations across various real-world datasets, including Google Local and Last.fm, benchmarking FairRec against established baselines such as the naive top-k and random-k recommendation methods. Key results demonstrate that:

• FairRec achieves high fairness across both producers and consumers with minimal loss in recommendation quality.
• The algorithm successfully minimizes disparities in producer exposures when compared to traditional consumer-centric approaches.
• While ensuring producer fairness, FairRec slightly reduces consumer utility compared to top-k, but manages this trade-off more effectively than other producer-oriented methods.

Practical Implications

The application of FairRec carries practical significance, especially for platforms like streaming services and online retailers, where producer visibility directly translates into economic opportunity. Ensuring a fairer distribution of exposure could mitigate potential legal challenges related to bias and fairness and foster a more sustainable ecosystem for both sides of the platform.

Future Directions

Looking ahead, addressing potential biases in attention models, particularly where placement bias affects exposure, could be a worthwhile avenue for extending this research. Integration of richer, real-time feedback loops could also enhance adaptability in dynamic marketplaces, providing tailored fairness enhancements.

In conclusion, FairRec stands as a nuanced and theoretically robust approach to balancing the fairness scales within two-sided platform ecosystems, contributing valuable insights and tools to the field of fair machine learning and algorithm design.