Community Detection Based on Readers' Borrowing Records
Xin Liu, E Haihong, Song Junde · 2013
Academic libraries have recently adopted recommender systems to provide personalized service for increased library-resource use and personalized educations. Readers' borrowing records help libraries to realize reader preferences and the recommender systems further provide book recommendations for readers. To apply the readers' borrowing records in recommendations, we give some inceptions in this paper. We find out that most of the people only borrow one or two books once a time and the interval between two successive borrowing records is usually shot as half a month. And by constructing the reader-reader similarity network, we find it have some characteristics: scale-free distribution, the small-world effect and strong community structure. And we propose three different algorithms to detect the communities in the reader-reader similarity network. At last, we compare the proposed algorithms to the existing one on the real world dataset.