Research on University Textbook Recommendation Based on the ISODATA Algorithm
Wenzheng Cai, Wenyan Zhu, Wenhao Zhu, Ke Zhang · 2024
This paper explores how to optimize university textbook recommendation systems using clustering analysis methods based on the ISODATA algorithm. It first introduces the basic principles of the ISODATA algorithm and its application in data clustering. Subsequently, by collecting and analyzing a large amount of data from teachers' teaching practices, an efficient textbook recommendation model is established. The research results show that the recommendation system based on the ISODATA algorithm can not only effectively improve students' learning outcomes but also provide data support for educational management departments, helping to optimize the allocation of teaching material resources and promote the continuous improvement of educational quality. This study provides new ideas and methods for the development of university textbook recommendation systems, which has important theoretical and practical significance.