Exploring author gender in book rating and recommendation
Article Properties
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LanguageEnglish
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DOI (url)
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Publication Date2021/02/04
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Indian UGC (journal)
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Refrences86
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Citations13
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Michael D. Ekstrand ORCID (unauthenticated)
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Daniel Kluver
Cite
Ekstrand, Michael D., and Daniel Kluver. “Exploring Author Gender in Book Rating and Recommendation”. User Modeling and User-Adapted Interaction, vol. 31, no. 3, 2021, pp. 377-20, https://doi.org/10.1007/s11257-020-09284-2.
Ekstrand, M. D., & Kluver, D. (2021). Exploring author gender in book rating and recommendation. User Modeling and User-Adapted Interaction, 31(3), 377-420. https://doi.org/10.1007/s11257-020-09284-2
Ekstrand, Michael D., and Daniel Kluver. “Exploring Author Gender in Book Rating and Recommendation”. User Modeling and User-Adapted Interaction 31, no. 3 (2021): 377-420. https://doi.org/10.1007/s11257-020-09284-2.
Ekstrand MD, Kluver D. Exploring author gender in book rating and recommendation. User Modeling and User-Adapted Interaction. 2021;31(3):377-420.
Journal Categories
Science
Mathematics
Instruments and machines
Electronic computers
Computer science
Science
Mathematics
Instruments and machines
Electronic computers
Computer science
Computer software
Technology
Electrical engineering
Electronics
Nuclear engineering
Electronics
Computer engineering
Computer hardware
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Refrences
| Title | Journal | Journal Categories | Citations | Publication Date |
|---|---|---|---|---|
Array programming with NumPy | Nature |
| 8,651 | 2020 |
Reader and author gender and genre in Goodreads | Journal of Librarianship and Information Science |
| 16 | 2019 |
| Stan: A Probabilistic Programming Language | Journal of Statistical Software |
| 3,343 | 2017 |
| Understanding the role of latent feature diversification on choice difficulty and satisfaction | User Modeling and User-Adapted Interaction |
| 30 | 2016 |
To Predict and Serve? | Significance | 144 | 2016 |
Citations
| Title | Journal | Journal Categories | Citations | Publication Date |
|---|---|---|---|---|
Enhancing Deliberation with Digital Democratic Innovations | Philosophy & Technology | 2024 | ||
| AI alignment: Assessing the global impact of recommender systems | Futures |
| 2024 | |
Distributionally-Informed Recommender System Evaluation | ACM Transactions on Recommender Systems | 2 | 2024 | |
FairGap: Fairness-Aware Recommendation via Generating Counterfactual Graph | ACM Transactions on Information Systems |
| 1 | 2024 |
Quantifying Bias in Hierarchical Category Systems | Open Mind |
| 2024 |
Citations Analysis
The category
Science: Science (General): Cybernetics: Information theory 4
is the most commonly referenced area in studies that cite this article. The first research to cite this article was titled Towards responsible media recommendation and was published in 2021. The most recent citation comes from a 2024 study titled AI alignment: Assessing the global impact of recommender systems. This article reached its peak citation in 2024, with 6 citations. It has been cited in 12 different journals, 33% of which are open access. Among related journals, the Frontiers in Big Data cited this research the most, with 2 citations. The chart below illustrates the annual citation trends for this article.