Reading promotion mode of university libraries in the context of information development

Abstract

Abstract This paper mainly proposes a technical route of user profiling based on multi-view clustering and constructs a multidimensional and multi-view reader feature system based on reader behavior data. According to the constructed reader feature system and the defects of the classical K-means algorithm in multi-view clustering, a multi-view dichotomous K-means algorithm based on Marxian distance is proposed. Using the proposed algorithm for multi-view clustering of readers, the user portrait features of readers can be obtained, and finally, a university library user portrait system is designed and implemented. By analyzing the behavior of college students through the user portrait technique, we get that the most preferred book genre of college students is literature accounting for 71.45%, followed by science and history with 57.64% and 51.37%, respectively. The most popular form of reading promotion among college students is classical film and television exhibitions, accounting for 63.73%, followed by lectures by celebrities and experts, accounting for 60.91%. The highest influence on college students going to the library is too many spare time activities, accounting for 64.38%, and not choosing books, accounting for 36.95%. These data can help libraries understand readers’ needs and provide help for reading accurate recommendations and services based on readers’ user profiles.