A model for book inquiry history analysis and book-acquisition recommendation of libraries

In the era of knowledge economy, the libraries play an important role for library users to maintain and provide a large number of book resources. In order to satisfy requirements of borrowers, the libraries have to purchase all kinds of new books on a regular time schedule. However, the borrowers' demands cannot be satisfied simply because of the limited number of librarians and thus the libraries require useful suggestions for book-acquisition. Traditionally, the book-acquisition recommendation applications are collected by library consultants and then evaluated by librarians. Under the circumstance, several pitfalls (e.g., only partial library borrowers realize the book-acquisition recommendation procedure or a lot of time and human efforts required) might occur. Therefore, this paper focuses on the development of a book-acquisition recommendation model for libraries to acquire the various library borrowers' demands based on book inquiry history under a library system.In addition to the book-acquisition recommendation model, a Web-based book-acquisition recommendation system is also developed and a demonstration case is applied to verify the performance of the proposed approach. Under the book-acquisition recommendation platform, the librarians can automatically derive the book-acquisition recommendation list to fit borrowers' requirements and the complicated recommendation processes for borrowers can also be reduced. The attempt of this research is to enhance the accuracy and efficiency of book-acquisition performance and therefore the book-acquisition tasks in library can be efficiently accomplished.

► Enhance the accuracy and efficiency of book-acquisition performance.
► This model is proposed to satisfy most borrowers' requirements of books.
► A Web-based book-acquisition recommendation system is also established.
► A real-world case is presented to examine applicability of proposed model/system.
► Verification results show that it is a high-performance book recommendation system.