Abstract
In recent years, the university libraries in China have acquired increasingly abundant electronic resources. However, the information silo phenomenon appears due to the lack of connection between university IT system and the community. Based on the book borrowing, favourite collection, comments and social relationship of students, this paper digs into the personalized interests of students, and promotes the design and implementation of a personalized recommender system. Specifically, the overall framework and recommender engine of the system were created based on the library data services. The modules in the system were also elaborated, and the recommendation results were verified by an offline test.
Highlights
With the explosive development of the IT industry, the information tidal wave has swept across universities in China
Despite the increasingly abundant electronic resources in universities, the information silo phenomenon appears due to the lack of connection between university IT system and the community
This paper introduces the relatively mature concept of recommender system in E-commerce and library database into the design and implementation of a personalized recommender system for university students [7]
Summary
With the explosive development of the IT industry, the information tidal wave has swept across universities in China. The shortage of teaching resources and hardware facilities have been alleviated, thanks to techniques like office automation, information management, curriculum management and digital libraries [1,2,3]. It is imperative for the universities to provide students with personalized services through integration of their E-campus information resources [46]. This paper introduces the relatively mature concept of recommender system in E-commerce and library database into the design and implementation of a personalized recommender system for university students [7]. The recommended modules can satisfy the demands of different users. Experimental results show that the proposed system, coupled with the library database, achieved desirable recommendation results [8,9,10]
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