# Hidden Author Bias in Book Recommendation

arXiv: https://arxiv.org/abs/2209.00371 · HTML: https://mlsanity.com/paper/2209.00371/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 10 of its 35 references that are in the map.
- First posted on arXiv 1 Sep 2022.

## Builds on (most cited first, 10 of 10)

- [No Classification without Representation: Assessing Geodiversity Issues in Open Data Sets for the Developing World](https://mlsanity.com/paper/1711.08536/) (2017)
- [How Algorithmic Confounding in Recommendation Systems Increases Homogeneity and Decreases Utility](https://mlsanity.com/paper/1710.11214/) (2017)
- [The Unfairness of Popularity Bias in Recommendation](https://mlsanity.com/paper/1907.13286/) (2019)
- [Exploring Author Gender in Book Rating and Recommendation](https://mlsanity.com/paper/1808.07586/) (2018)
- [The Unfairness of Popularity Bias in Music Recommendation: A Reproducibility Study](https://mlsanity.com/paper/1912.04696/) (2019)
- [Popularity Bias Is Not Always Evil: Disentangling Benign and Harmful Bias for Recommendation](https://mlsanity.com/paper/2109.07946/) (2021)
- [The Unfairness of Popularity Bias in Book Recommendation](https://mlsanity.com/paper/2202.13446/) (2022)
- [Popularity Bias in Recommendation: A Multi-stakeholder Perspective](https://mlsanity.com/paper/2008.08551/) (2020)
- [Analyzing Race and Country of Citizenship Bias in Wikidata](https://mlsanity.com/paper/2108.05412/) (2021)
- [Exploring and Mitigating Gender Bias in Recommender Systems with Explicit Feedback](https://mlsanity.com/paper/2112.02530/) (2021)

## Abstract (arXiv)

Collaborative filtering algorithms have the advantage of not requiring sensitive user or item information to provide recommendations. However, they still suffer from fairness related issues, like popularity bias. In this work, we argue that popularity bias often leads to other biases that are not obvious when additional user or item information is not provided to the researcher. We examine our hypothesis in the book recommendation case on a commonly used dataset with book ratings. We enrich it with author information using publicly available external sources. We find that popular books are mainly written by US citizens in the dataset, and that these books tend to be recommended disproportionally by popular collaborative filtering algorithms compared to the users' profiles. We conclude that the societal implications of popularity bias should be further examined by the scholar community.