Book Recommendation for Library Automation Use in School Libraries by Multi Features of Support Vector Machine

Abstract

This paper proposed the algorithms of book recommendation for the open source of library automation by using machine learning method of support vector machine. The algorithms consist of using multiple features (1) similarity measures for book title (2) The DDC for systematic arrangement combination of Association Rule Mining (3) similarity measures for bibliographic information of book. To evaluate, we used both qualitative and quantitative data. For qualitative, sixty four students of Banpasao Chiang Mai school reported the satisfaction questionnaire and interview. For Quantitative, we used web monitoring and precision measures to effectively use the system. The results show that books recommended by our algorithms can suggest books to students “Very interested” and “interested” by 14.5% and 22.5% and improve usage of the OPAC system’s highest average of 52 per day. Therefore, these systems suitable for library automation of Thai language and small library with not much book resource.