Chinese book recommendation for elementary students: a comparison between ML and DL methods

Rui Sun; Chuyang Wei; Zepeng Li; Jialin Zhao; Shicai Li; Jun Ma

doi:10.1117/12.2639345

15 July 2022 Chinese book recommendation for elementary students: a comparison between ML and DL methods

Rui Sun, Chuyang Wei, Zepeng Li, Jialin Zhao, Shicai Li, Jun Ma

Proceedings Volume 12258, International Conference on Neural Networks, Information, and Communication Engineering (NNICE 2022); 122580E (2022) https://doi.org/10.1117/12.2639345
Event: International Conference on Neural Networks, Information, and Communication Engineering (NNICE 2022), 2022, Qingdao, China

Abstract

Experience-based book recommendation systems suffer from blurred boundaries and unguaranteed quality. In China, there is no reasonable and credible book recommendation system for elementary school students based on big data and expert systems. The basis of book recommendation is text classification. In this paper, we first construct a hierarchy dictionary to encode books according to the textbook read by pupils in different grades. We then classify extra-curricular books into lower grade, upper grade, and non-elementary classes. Four machine learning methods and a deep learning algorithm are used to classify the text. The model metrics are evaluated by Accuracy, Recall, F1-score. Among them, both deep learning and machine learning have a good performance on binary classification tasks, with the accuracy of the LR method reaching 98.5%. This reflects the value of our construction of a hierarchical vocabulary. In addition, accuracy for the triple classification task generally achieves 75%.

Citation Download Citation

Rui Sun, Chuyang Wei, Zepeng Li, Jialin Zhao, Shicai Li, and Jun Ma "Chinese book recommendation for elementary students: a comparison between ML and DL methods", Proc. SPIE 12258, International Conference on Neural Networks, Information, and Communication Engineering (NNICE 2022), 122580E (15 July 2022); https://doi.org/10.1117/12.2639345

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KEYWORDS

Machine learning

Binary data

Computer programming

Data modeling

Classification systems

Performance modeling

Model-based design

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