Developing a novel recommender network-based ranking mechanism for library book acquisition

Fan Wu, Ya Han Hu, Ping Rong Wang

Research output: Contribution to journalArticlepeer-review

6 Scopus citations

Abstract

Purpose - Most academic libraries provide book recommendation services to enable readers to recommend books to the libraries. To facilitate decision-making in book acquisition, this study aimed to develop a method to determine the ranking of the recommended books based on the recommender network. Design/methodology/approach - The recommender network was conducted to establish relationships among book recommenders and their similar readers by using circulation records. Furthermore, social computing techniques were used to evaluate the degree of representativeness of the recommenders and subsequently applied as a criterion to rank the recommended books. Empirical studies were performed to demonstrate the effectiveness of the proposed ranking system. The Spearman's correlation coefficients between the proposed ranking system and the ranking obtained using reader circulation statistics were used as performance measure. Findings - The ranking calculated using the proposed ranking mechanism was highly and moderately correlated to the ranking obtained using reader circulation statistics. The ranking of recommended books by the librarians was moderately and poorly correlated to the ranking calculated using reader circulation statistics. Practical implications - The book recommender can be used to improve the accuracy of book recommendations. Originality/value - This study is the first that considers the recommender network on library book acquisition. The results also show that the proposed ranking mechanism can facilitate effective book-acquisition decisions in libraries.

Original languageEnglish
Pages (from-to)50-68
Number of pages19
JournalElectronic Library
Volume35
Issue number1
DOIs
StatePublished - 2017

Keywords

  • Academic libraries
  • Book acquisition
  • Decision support systems
  • Recommendation
  • Recommender network

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