A Multi-Objective Optimization Method for Achieving Two-sided Fairness in E-commerce Recommendation
release_vjazqauyuzdyrait4g534cyqpu
by
Haolun Wu, Chen Ma, Bhaskar Mitra, Fernando Diaz, Xue Liu
2021
Abstract
Two-sided marketplaces are an important component of many existing Internet
services like Airbnb and Amazon, which have both consumers (e.g. users) and
producers (e.g. retailers). Traditionally, the recommendation system in these
platforms mainly focuses on maximizing customer satisfaction by recommending
the most relevant items based on the learned user preference. However, it has
been shown in previous works that solely optimizing the satisfaction of
customers may lead to unfair exposure of items, which jeopardizes the benefits
of producers. To tackle this problem, we propose a fairness-aware
recommendation framework by using multi-objective optimization, Multi-FR, to
adaptively balance the objectives between consumers and producers. In
particular, Multi-FR adopts the multi-gradient descent to generate a Pareto set
of solutions, where the most appropriate one is selected from the Pareto set.
In addition, four fairness metrics/constraints are applied to make the
recommendation results on both the consumer and producer side fair. We
extensively evaluate our model on three real-world datasets, comparing with
grid-search methods and using a variety of performance metrics. The
experimental results demonstrate that Multi-FR can improve the recommendation
fairness on both the consumer and producer side with little drop in
recommendation quality, also outperforming several state-of-the-art fair
ranking approaches.
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