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The interlibrary loan (ILL) service is essential to a multi-campus university library. This article builds a dynamic collection optimization model based on readers’ needs using ILL data. This article first examined the status quo and methods of ILL. Then, refers to some existing algorithms of bipartite matching and recommendation such as network-based inference (NBI). Based on the above analysis, this article builds a model to optimize university library collection. With the loaning data of Soochow Academic Library from 2013 as the training data, an optimizing case was performed. The 2469 interlibrary loan records including readers and book they borrow from other library in 2014 was used for testing the effects of the model. The model is able to identify potential ILL demands and improve the efficiency of book circulation.

The Collection and Service Optimization of China’s Academic Library ILL Based on Bipartite Matching: A Case Study of Soochow University