# Exploring Author Gender in Book Rating and Recommendation

arXiv: https://arxiv.org/abs/1808.07586 · HTML: https://mlsanity.com/paper/1808.07586/

## Key facts

- Cited by 29 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 Aug 2018.

## Built on by (most cited first, 29 of 29)

- [EDITS: Modeling and Mitigating Data Bias for Graph Neural Networks](https://mlsanity.com/paper/2108.05233/) (2021)
- [AutoDebias: Learning to Debias for Recommendation](https://mlsanity.com/paper/2105.04170/) (2021)
- [Socially Responsible AI Algorithms: Issues, Purposes, and Challenges](https://mlsanity.com/paper/2101.02032/) (2021)
- [Unlearning Protected User Attributes in Recommendations with Adversarial Training](https://mlsanity.com/paper/2206.04500/) (2022)
- [Improving Recommendation Fairness via Data Augmentation](https://mlsanity.com/paper/2302.06333/) (2023)
- [Achieving Counterfactual Fairness for Causal Bandit](https://mlsanity.com/paper/2109.10458/) (2021)
- [Estimation of Fair Ranking Metrics with Incomplete Judgments](https://mlsanity.com/paper/2108.05152/) (2021)
- [Revisiting Popularity and Demographic Biases in Recommender Evaluation and Effectiveness](https://mlsanity.com/paper/2110.08353/) (2021)
- [Fairness and Transparency in Recommendation: The Users' Perspective](https://mlsanity.com/paper/2103.08786/) (2021)
- [Quantifying Availability and Discovery in Recommender Systems via Stochastic Reachability](https://mlsanity.com/paper/2107.00833/) (2021)
- [Cascaded Debiasing: Studying the Cumulative Effect of Multiple Fairness-Enhancing Interventions](https://mlsanity.com/paper/2202.03734/) (2022)
- [Maximizing Submodular Functions for Recommendation in the Presence of Biases](https://mlsanity.com/paper/2305.02806/) (2023)
- [Achieving Fairness Across Local and Global Models in Federated Learning](https://mlsanity.com/paper/2406.17102/) (2024)
- [When Newer is Not Better: Does Deep Learning Really Benefit Recommendation From Implicit Feedback?](https://mlsanity.com/paper/2305.01801/) (2023)
- [Path-Specific Causal Reasoning for Fairness-aware Cognitive Diagnosis](https://mlsanity.com/paper/2406.03064/) (2024)
- [Algorithms are not neutral: Bias in collaborative filtering](https://mlsanity.com/paper/2105.01031/) (2021)
- [An Adaptive Boosting Technique to Mitigate Popularity Bias in Recommender System](https://mlsanity.com/paper/2109.05677/) (2021)
- [Diversely Regularized Matrix Factorization for Accurate and Aggregately Diversified Recommendation](https://mlsanity.com/paper/2211.01328/) (2022)
- [Break Out of a Pigeonhole: A Unified Framework for Examining Miscalibration, Bias, and Stereotype in Recommender Systems](https://mlsanity.com/paper/2312.17443/) (2023)
- [DifFaiRec: Generative Fair Recommender with Conditional Diffusion Model](https://mlsanity.com/paper/2410.02791/) (2024)
- [Overhead-free User-side Recommender Systems](https://mlsanity.com/paper/2411.07589/) (2024)
- [Datasets for Navigating Sensitive Topics in Recommendation Systems](https://mlsanity.com/paper/2509.07269/) (2025)
- [MOReGIn: Multi-Objective Recommendation at the Global and Individual Levels](https://mlsanity.com/paper/2401.12593/) (2024)
- [Polarization by Default: Auditing Recommendation Bias in LLM-Based Content Curation](https://mlsanity.com/paper/2604.15937/) (2026)
- [Hidden Author Bias in Book Recommendation](https://mlsanity.com/paper/2209.00371/) (2022)
- [Fuck the Algorithm: Conceptual Issues in Algorithmic Bias](https://mlsanity.com/paper/2505.13509/) (2025)
- [SOMtime the World Ain't Fair: Violating Fairness Using Self-Organizing Maps](https://mlsanity.com/paper/2602.18201/) (2026)
- [Who Decides What Is Harmful? Content Moderation Policy Through A Multi-Agent Personalised Inference Framework](https://mlsanity.com/paper/2605.01416/) (2026)
- [AgentRecommender: LLM Agents Enable Customizable Recommender Systems on the User Side](https://mlsanity.com/paper/2609.31166/) (2026)

## Abstract (arXiv)

Collaborative filtering algorithms find useful patterns in rating and consumption data and exploit these patterns to guide users to good items. Many of the patterns in rating datasets reflect important real-world differences between the various users and items in the data; other patterns may be irrelevant or possibly undesirable for social or ethical reasons, particularly if they reflect undesired discrimination, such as discrimination in publishing or purchasing against authors who are women or ethnic minorities. In this work, we examine the response of collaborative filtering recommender algorithms to the distribution of their input data with respect to a dimension of social concern, namely content creator gender. Using publicly-available book ratings data, we measure the distribution of the genders of the authors of books in user rating profiles and recommendation lists produced from this data. We find that common collaborative filtering algorithms differ in the gender distribution of their recommendation lists, and in the relationship of that output distribution to user profile distribution.