Research on Fast Recommendation Algorithm of Library Personalized Information Based on Density Clustering

Abstract

In order to improve the accuracy and efficiency of library information recommendation, this paper proposes a fast recommendation algorithm for library personalized information based on density clustering. According to the analysis of the clustering principle, the algorithm achieves the clustering of library information by designing density interval function. Then, the collection priority of library personalized information is judged, and the library personalized information is recommended quickly by designing tags according to the library users’ preferences. Experimental results show that the recommendation accuracy and F value of the proposed algorithm are higher than those of the two traditional algorithms, and its coverage rate is higher and the mean absolute error is lower, indicating that the proposed algorithm effectively achieves the design expectation.