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@inproceedings{ekstrand_exploring_2018,
	address = {New York, NY, USA},
	title = {Exploring author gender in book rating and recommendation},
	url = {https://dl.acm.org/doi/10.1145/3240323.3240373},
	doi = {10.1145/3240323.3240373},
	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
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.},
	publisher = {ACM},
	author = {Ekstrand, Michael D and Tian, Mucun and Kazi, Mohammed R Imran and Mehrpouyan, Hoda and Kluver, Daniel},
	month = sep,
	year = {2018},
}