Machine-learning Recommender Systems Can Inform Collection Development Decisions

• Published in: Evidence based library and information practice Vol. 19; no. 2; pp. 133 - 135
• Main Author: Hancock, Kristy
• Format: Book Review Journal Article
• Language: English
• Published: Edmonton University of Alberta Library 2024; University of Alberta, Learning Services; University of Alberta
• Subjects: Algorithms; Collection development; Interlibrary loans; Librarians; Library materials; Novels; Recommender systems; New York; United States > US
• ISSN: 1715-720X; 1715-720X
• DOI: 10.18438/eblip30521

A Review of: Xiao, J., & Gao, W. (2020). Connecting the dots: reader ratings, bibliographic data, and machine-learning algorithms for monograph selection. The Serials Librarian, 78(1-4), 117-122. https://doi.org/10.1080/0361526X.2020.1707599 Objective – To illustrate how machine-learning book recommender systems can help librarians make collection development decisions. Design – Data analysis of publicly available book sales rankings and reader ratings. Setting – The internet. Subjects – 192 New York Times hardcover fiction best seller titles from 2018, and 1,367 Goodreads ratings posted in 2018. Methods – Data were collected using Application Programming Interfaces. The researchers retrieved weekly hardcover fiction best seller rankings published by the New York Times in 2018 in CSV file format. All 52 files, each containing bibliographic data for 15 hardcover fiction titles, were combined and duplicate titles removed, resulting in 192 unique best seller titles. The researchers retrieved reader ratings of the 192 best seller titles from Goodreads. The ratings were limited to those posted in 2018 by the top Goodreads reviewers. A Bayes estimator produced a list of the top ten highest rated New York Times best sellers. The researchers built the recommender system using Python and employed several content-based and collaborative filtering recommender techniques (e.g., cosine similarity, term frequency-inverse document frequency, and matrix factorization algorithms) to identify novels similar to the highest rated best sellers. Main Results – Each recommender technique generated a different list of novels. Conclusion – The main finding from this study is that recommender systems can simplify collection development for librarians and facilitate greater access to relevant library materials for users. Academic libraries can use the same recommender techniques employed in the study to identify titles similar to highly circulated monographs or frequently requested interlibrary loans. There are several limitations to using recommender systems in libraries, including privacy concerns when analyzing user behaviour data and potential biases in machine-learning algorithms.