Evaluating the Performance of Different Models for Children’s Book Recommendations

Bachelor thesis (2023)

Authors

Z. Qiu Electrical Engineering, Mathematics and Computer Science

Contributors

M.S. Pera Web Information Systems - EEMCS (supervisor 1)
W.P. Brinkman Interactive Intelligence - EEMCS (supervisor 2)
Faculty
Electrical Engineering, Mathematics and Computer Science
Copyright: © 2023 Ziang Qiu
Recommender System Information Retrieval Children
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Published Date

03-02-2023

Language

English

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Faculty

Electrical Engineering, Mathematics and Computer Science

Abstract

Recently, a few children-centered recommendation systems have been created and evaluated. How- ever, these systems required user interaction to cre- ate ground truth to evaluate the result. This research aims to compare some of the traditional recommen- dation models and explore which trait could impact the recommendation process most for different age group users. The result shows the children friendly model does not achieve higher accuracy than the traditional recommendation model. But the book length model and emotion analysis model shows the potential of a good RS that can help children choose books, and cover image recommendation models are only working with younger age