Title: CBRec: a book recommendation system for children using the matrix factorisation and content-based filtering approaches
Authors: Yiu-Kai Ng
Addresses: Computer Science Department, Brigham Young University, Provo, Utah, 84602, USA
Abstract: Promoting good reading habits among children is essential, given the enormous influence of reading on students' development as learners and members of the society. Unfortunately, very few (children) websites or online applications recommend books to children, even though they can play a significant role in encouraging children to read. Given that a few popular book websites suggest books to children based on the popularity of books or rankings on books, they are not customised/personalised for each individual user and likely recommend books that users do not want or like. We have integrated the matrix factorisation approach and the content-based approach, in addition to predicting the grade levels of books, to recommend books for children. Recent research works have demonstrated that a hybrid approach, which combines different filtering approaches, is more effective in making recommendations. Conducted empirical study has verified the effectiveness of our proposed children book recommendation system.
Keywords: book recommendation; matrix factorisation; content analysis; children.
International Journal of Business Intelligence and Data Mining, 2020 Vol.16 No.2, pp.129 - 149
Received: 16 May 2017
Accepted: 12 Aug 2017
Published online: 30 Jan 2020 *