A Serendipity Recommendation Method for Book Categories Using BERT to Strengthen the Web Service of the Book
DOI:
https://doi.org/10.13052/jwe1540-9589.2422Keywords:
Web service, book category, BERT, serendipity, recommendationAbstract
In the field of book search, research on a web service-based user-customized book recommendation system is being conducted to respond to increasingly diverse user requirements. The collaborative filtering algorithm, which is mainly used for book recommendation, has a problem in that it is difficult to reflect the user’s recent interest without considering the changes in preference over time, and the user’s satisfaction decreases because it repeatedly recommends only similar items.
In this paper, we propose a book recommendation method using category similarity based on deep learning. The proposed method is to predict books to be used next time by inputting users’ past and current book usage history through BERT, a natural language processing model, and to recommend popular books in other categories with high similarity to the predicted book category in the BERT model to reflect serendipity. This method reflects serendipity, which can lead to users’ recent interests and practical preferences, so that recommendation accuracy and user satisfaction can be satisfied at the same time.
Downloads
References
J. Son, S. B. Kim, H. Kim, S. Cho, “Review and Analysis of Recommender Systems,” Journal of the Korean Institute of Industrial Engineers, Vol. 41, No. 2, pp. 185–208, 2015.
K. Song., I. Moon, “Introduction to recent recommendation system research and future works,” Communications of the Korean Institute of Information Scientists and Engineer, Vol. 39, No. 3, pp. 16–23, 2021.
S. Kim, Y. J. Roh, M. R. Kim, “A Narrative Study on User Satisfaction of Book Recommendation Service based on Association Analysis,” Journal of Korean Library and Information Science Society, Vol. 52, No. 3, pp. 287–311, 2021.
S. Wang, L. Hu, Y. Wang, L. Cao, Q. Z. Sheng, M. Orgun, “Sequential recommender systems: challenges, progress and prospects,” arXiv preprint arXiv:2001.04830, 2019.
P. Adamopoulos, A. Tuzhilin, “On unexpectedness in recommender systems: Or how to better expect the unexpected,” ACM Transactions on Intelligent Systems and Technology (TIST), Vol. 5, No. 4, pp. 1–32, 2014.
D. Kotkov, J. Veijalainen, S. Wang, “Challenges of serendipity in recommender systems,” In International conference on web information systems and technologies, SCITEPRESS, 2016.
M. Quadrana, P. Cremonesi, D. Jannach, “Sequence-aware recommender systems,” ACM Computing Surveys (CSUR), Vol. 51, No. 4, pp. 1–36, 2018.
H. Fang, D. Zhang, Y. Shu, G. Guo, “Deep learning for sequential recommendation: Algorithms, influential factors, and evaluations,” ACM Transactions on Information Systems (TOIS), Vol. 39, No. 1, pp. 1–42, 2020.
B. Hidasi, A. Karatzoglou, L. Baltrunas, D. Tikk, “Session-based recommendations with recurrent neural networks,” arXiv preprint arXiv:1511.06939, 2015.
M. Quadrana, A. Karatzoglou, B. Hidasi, P. Cremonesi, “Personalizing session-based recommendations with hierarchical recurrent neural networks,” In proceedings of the Eleventh ACM Conference on Recommender Systems, pp. 130–137, 2017.
C. C. Aggarwal, “Recommender systems,” Cham: Springer International Publishing, Vol. 1, 2016
Y. Hu, Y. Koren, C. Volinsky, “Collaborative filtering for implicit feedback datasets,” In 2008 Eighth IEEE international conference on data mining, pp. 263–272, IEEE, 2008.
J. Devlin, M. W. Chang, K. Lee, K. Toutanova, “Bert:Pre-training of deep bidirectional transformers for language understanding,” arXiv preprint arXiv:1810.04805, 2018.
X. He, L. Liao, H. Zhang, L. Nie, X. Hu, T. S. Chua, “Neural collaborative filtering,” In Proceedings of the 26th international conference on world wide web, pp. 173–182, 2017.
G. Shani, A. Gunawardana, “Evaluating recommendation systems,” In Recommender systems handbook (pp. 257–297). Springer, Boston, MA, 2011.
Y. C. Zhang, D. Ó. Séaghdha, D. Quercia, T. Jambor, “Auralist:introducing serendipity into music re
