Paper Info
Title
An Active Recommendation Approach to Improve Book-Acquisition Process.
Abstract
In the current book-acquisition recommendation process of libraries (e.g, university’s library), only a part of borrowers actively recommends purchasing books and the book recommendation mostly needs to be processed artificially; thus, most borrowers’ requirements can not be satisfied and the book acquisition efficiency is unsound. Therefore, this paper attempts to develop a book-acquisition recommendation model and system based on text mining technology and Internet technology to provide librarians for suggestions of book-acquisition. The proposed book-acquisition recommendation model includes three kernel modules namely Keyword Density Thesaurus (KDT), Keyword Sequence Thesaurus (KST) and Keyword-Book Mapping (KBM) modules. The book inquiry strings inquired from borrowers can be collected for keyword extraction via KDT and KST modules. After that, the extracted keywords are matching with the book database of bookseller to obtain the recommended books and the recommendation list for book-acquisition can be generated via KBM module. In addition to the book-acquisition recommendation model, a Web-based book-acquisition recommendation system is also developed. 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 simplified. In brief, the book-acquisition recommendation process of this paper is of system-based active recommendation and the book recommendation list doesn’t need to be collected artificially. Moreover, the generated book-acquisition recommendation list can meet most borrowers’ requirements, and the efficiency and effectiveness of the library on book-acquisition can be improved.
Year
Venue
Keywords
2012
IJEBM
library,text mining,knowledge management
Field
DocType
Volume
Keyword density,Recommender system,World Wide Web,Information retrieval,Keyword extraction,Purchasing,Engineering,Recommendation model,The Internet
Journal
10
Issue
Citations 
PageRank 
2
1
0.48
References 
Authors
13
1
Name
Order
Citations
PageRank
Shih-Ting Yang1254.46