Document Type

Conference Proceeding

Publication Date



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 gender or ethnic discrimination in publishing. 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.


The related computer script can be found here:

Published article derived from this paper can be found here:

Additional Resources related to this work can be found at:

Copyright Statement

This document was originally published in RecSys '18: Proceedigns of the 12th ACM Conference on Recommender Systems by the Association for Computing Machinery. Copyright restrictions may apply. (69194 kB)
Scripts for Exploring Author Gender in Book Rating and Recommendation