Lost in Translation

Abstract

Recommender Systems are key instruments that are constantly being employed in the online environment as a method of connecting users and items. Due to the resulting personalised suggestions, users benefit from quickened decision making, however, such systems can also introduce unwanted side-effects, by reinforcing existing biases or limiting exposure to diverse content. While much of the research surrounding unfairness and its effects has been conducted specifically to benefit the users, the creators of the distributed items, namely the providers, can also be subject to inequity. From the perspective of a provider, garnering visibility and exposure of their items through these systems converts into revenue. Recommender Systems can be a positive means in sectors where, historically, groups of providers have been disadvantaged from reaching their desired consumers, although if mishandled can become an additional hindrance. Amongst the many relevant domains, the book industry is a clear example where authors have been discriminated based on traits unrelated to the quality of their writing. Publishers have manifested an adversity towards translated works leading to an innate disadvantage when it comes to their distribution and marketing. Still, the fairness of how recommender systems handle translated works when competing with other books remains unexplored. To address this gap, we conduct an empirical exploration of several state of the art recommendation algorithms, evaluating their performance with respects to accuracy and provider fairness. This allows us to discern if any of the algorithms are a helpful conduit or a harmful one. Upon identifying inequity concerning foreign authors, we probe the ability of mitigating it on an algorithmic level, by applying a reranking method. Outcomes stemming from this work, reveal high representation of books available in English in recommendations, while highlighting the advantages of foreign manuscripts with translations over those without. The outlined findings inform future design of Recommender Systems and their provider fairness evaluation.