Bias in book recommendation: A survey

Automated book recommendation is a compelling application given the rich and long-standing human tradition of writing and reading. The documented discrimination in the publishing world raises questions on the types of bias that can arise when algorithms are trained on user-book interactions. Various papers have studied recommender systems bias based on datasets from platforms where users share their opinions on books. In this paper, we conduct the first survey of computer science research on bias in book recommender systems. We survey 40 papers from top conferences and journals using the structured PRISMA methodology and provide an overview of the field. We map the existing literature into bias types, subjects and actions, and present key metrics and bias mitigation methods. We find that there is limited targeted research, fragmented evaluation practices, and a lack of engagement with the unique characteristics of the literary domain. Finally, we propose avenues for future work and make concrete suggestions for interdisciplinary research.