Objectives In this study, a personalized book system is designed for elementary school students based on machine learning technology, and a hybrid recommendation engine is developed. In addition, the purpose is to verify the performance through performance analysis of the developed recommended engine. Methods To achieve the above purpose, a toaping reading service with 9,000 members was used. 5,979,479 reading and reading activities were targeted, and a book recommendation system was developed using LightFM. The ranking for the activities was applied by applying the weight of each activity to the processing of Korean and various reading activities were applied. Results Performance analysis of the developed system was conducted. Performance analysis was conducted on AUC Score, Accuracy, Precison, Recall, and F1-Score, and as a result, 99.9%, 95.1%, 92.5%, 86.3%, and 89.3%, respectively. It was confirmed that the recommended book reflects the student's interest. Conclusions Through the results of the performance analysis, it was confirmed that the books selected by individual students and the books recommended by the developed recommendation engine were similar. It was confirmed that for elementary school students, book recommendations according to each student's area of interest should be made in addition to book recommendations by experts or related organizations. In addition, in order to improve the completeness of the system developed through this study, additional analysis and reflection of big data on book loans by public institutions are needed, and various additional studies are needed to recommend customized books.