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Conference proceeding
Exploring Author Gender in Book Rating and Recommendation
12TH ACM CONFERENCE ON RECOMMENDER SYSTEMS (RECSYS), pp 242-250
01 Jan 2018
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.
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Details
- Title
- Exploring Author Gender in Book Rating and Recommendation
- Creators
- Michael D. Ekstrand - Boise State UniversityMucun Tian - Boise State UniversityMohammed R. Imran Kazi - Texas State Univ, Dept Comp Sci, San Marcos, TX USAHoda Mehrpouyan - Boise State UniversityDaniel Kluver - Macalester CollegeACM
- Publication Details
- 12TH ACM CONFERENCE ON RECOMMENDER SYSTEMS (RECSYS), pp 242-250
- Publisher
- Assoc Computing Machinery
- Number of pages
- 9
- Resource Type
- Conference proceeding
- Language
- English
- Academic Unit
- Information Science
- Web of Science ID
- WOS:000458675100034
- Scopus ID
- 2-s2.0-85056759148
- Other Identifier
- 991021818387604721
InCites Highlights
Data related to this publication, from InCites Benchmarking & Analytics tool:
- Collaboration types
- Domestic collaboration
- Web of Science research areas
- Computer Science, Artificial Intelligence
- Computer Science, Information Systems
- Computer Science, Theory & Methods