Abstract
At present, most recommendation systems in libraries are content-based and collaborative filtering-based, but these recommendation systems ignore the deep-seated characteristics of readers' personalized information. In order to improve the function of Library recommendation system, this paper proposes a library recommendation system based on restricted Boltzmann machine and collaborative filtering algorithm, and simulates the performance of the algorithm. The results show that the proposed algorithm has good application effect.
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