Bias in book recommendation: A case study on the danish public libraries

Public libraries can potentially benefit by automated recommendation to increase patron engagement. Unlike commercial platforms, these publicly funded services have a responsibility to remain alert to biases that emerge in algorithmic recommendations. In this work, we conduct a systematic evaluation of popularity and author nationality bias in Booklens, a non-personalized item-to-item recommender used in Danish public libraries that receives loans, clicks, mood tags, and creator name as input. We prompt the system with a set of 10,000 books of varying popularity and author nationality and analyze the resulting recommendations under different parameter configurations controlling (e.g., recommendation list length). For each configuration, we measure overall popularity bias and resulting bias toward authors of different nationalities. Our analysis reveals that popularity strongly drives recommendation outcomes, with up to a 40% relative decrease in exposure for less popular nationalities depending on the parameter setting. We also show that certain configurations significantly amplify or decrease these disparities. These results highlight the need for systematic bias analysis to be a structural component of recommender systems evaluation, supporting libraries in aligning algorithmic behavior with social and cultural missions.

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