Fairness Embedded Adaptive Recommender System: A Conceptual Framework

Abstract

In the current fast paced and constantly changing environment, companies should ensure that their way of interacting with user is both relevant and highly adaptive. In order to stay competitive, companies should invest in state-ofthe-art technologies that optimize the relationship with the user using increasingly available data. The most popular applications used to develop user relationship are Recommender Systems. The vast majority of the traditional recommender system considers recommendation as a static procedure and focus on a specific type of recommendation, being not very agile in adapting to new situations. Also, when implementing a Recommender System there is the need to ensure fairness in the way decisions are made upon customer data. In this paper, it is proposed a novel Reinforcement Learning-based recommender system that is highly adaptive to changes in customer behavior and focuses on ensuring both producer and consumer fairness, Fairness Embedded Adaptive Recommender System (FEARS). The approach overcomes Reinforcement Learning's main drawback in recommendation area by using a small, but meaningful action space. Also, there are presented two fairness metrics, their calculation and adaptation for usage with Reinforcement Learning, this way ensuring that the system gets to the optimal trade-off between personalization and fairness.