Exploring Author Gender in Book Rating and Recommendation

TL;DR

This paper investigates how collaborative filtering recommender systems reflect and potentially reinforce gender biases in book author representation, analyzing the impact of input data gender distribution on recommendations.

Contribution

It provides an empirical analysis of gender bias in book recommendations and highlights differences among algorithms in handling author gender distribution.

Findings

Different algorithms produce varying gender distributions in recommendations.

Recommendation outputs often reflect the gender distribution of user profiles.

Some algorithms may perpetuate or mitigate gender bias in recommendations.

Abstract

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.