Conference Paper: “Exploring Author Gender in Book Rating and Recommendation”

The following paper will be presented at RecSys ’18, October 2–7, 2018 in Vancouver, BC.
Title
Exploring Author Gender in Book Rating and Recommendation
Authors
Michael D. Ekstrand
Boise State University
Mucun Tian
Boise State University
Mohammed R. Imran Kazi
Texas State University San Marcos
Hoda Mehrpouyan
Boise State University
Daniel Kluver
Macalester College
Source
via arXiv
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.

Direct to Full Text Article
9 pages; PDF.

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