Characterizing provider fairness in content-based e-service intelligent recommendation

Fairness is currently regarded as a relevant dimension towards the goal of reaching trustworthy artificial intelligence-based systems. In recommender systems, fairness is focused on addressing biases that may disproportionately benefit or harm certain classes of users and items. In this contribution we are interested on provider fairness, which aims at guaranteeing that the providers of the items would have the same chance for the exposure of their items in the final recommendation lists. Particularly, we will be focused on characterizing the provider fairness associated to intelligent content-based recommendation used for suggesting e-services in a marketplace environment in the region of Extremadura, Spain. Herein, the generalized cross-entropy has been used as metric for characterizing fairness associated to both basic and latent dirichlet allocation (LDA)-based content-based recommendation. As main findings, our study has indicated that the recommendation based on latent dirichlet allocation might lead to better fairness values for those providers with larger number of e-services. For the managerial viewpoint, it suggests that a higher presence in online platforms of the products, might guarantee better associated fairness values. As far as we know, this contribution presents one of the first efforts on evaluating provider fairness recommendation in a concrete e-service scenario.