Browse > Article
http://dx.doi.org/10.6109/jkiice.2022.26.9.1293

A Comparative Analysis of Personalized Recommended Model Performance Using Online Shopping Mall Data  

Oh, Jaedong (Aritificial Intelligence Convergence, Sungkyunkwan University)
Oh, Ha-young (College of Computing and Informatics, Sungkyunkwan University)
Publication Information
Journal of the Korea Institute of Information and Communication Engineering / v.26, no.9, 2022 , pp. 1293-1304 More about this Journal
Abstract
The personalization recommendation system means analyzing each individual's interests or preferences and recommending information or products accordingly. These personalized recommendations can reduce the time consumers spend searching for information by accessing the products they need more quickly, and companies can increase corporate profits by recommending appropriate products that meet their needs. In this study, products are recommended to consumers using collaborative filtering, matrix factorization, and deep learning, which are representative personalization recommendation techniques. To this end, the data set after purchasing shopping mall products, which is raw data, is pre-processed in the form of transmitting the data set to the input of the recommended system, and the pre-processed data set is analyzed from various angles. In addition, each model performs verification and performance comparison on the recommended results, and explores the model with optimal performance, suggesting which model should be used when building the recommendation system at the mall.
Keywords
Recommendation System; Collaborative Filtering; Matrix Factorization; Deep Learning;
Citations & Related Records
연도 인용수 순위
  • Reference
1 K. Sun, T. Qian, T. Chen, Y. Liang, Q. V.H. Nguyen, and H. Yin, "Where to Go Next: Modeling Long- and Short-Term User Preferences for Point-of-Interest Recommendation", In Proceedings of the AAAI Conference on Artificial Intelligence, New York, USA. pp. 214-221, 2020.
2 J. Son, S. B. Kim, H. Kim, and S. Cho, "Review and Analysis of Recommender Systems," Journal of Korean Institute of Industrial Engineers, vol. 41, no. 2. pp. 185-208, Apr. 2015.   DOI
3 S. Y. Cho, J. E. Choi, K. H. Lee, and H. W. Kim, "An Online Review Mining Approach to a Recommendation System," Information Systems Review, vol. 17, no. 3, pp. 95-111, Dec. 2015.   DOI
4 I. Im, Personalization Recommendation System Using Python, 1st ed. Seoul: CRbooks, 2020.
5 Amazon's Product Recommendation System In 2021: How Does The Algorithm Of The eCommerce Giant Work? [Internet]. Available: https://recostream.com/blog/amazon-recommendation-system.
6 S. S. Choudhury, S. N. Mohanty, and A. K. Jagadev, "Multimodal trust based recommender system with machine learning approaches for movie recommendation," International Journal of Information Technology, vol. 13, no. 2, pp. 475-482, Jan. 2021.   DOI
7 Recommender Systems: What Long-Tail tells ? [Internet]. Available: https://medium.com/@kyasar.mail/recommender-systems-what-long-tail-tells-91680f10a5b2.
8 M. Naumov, D. Mudigere, H. M. Shi, J. Huang, N. Sundaraman, J. Park, X. Wang, U. Gupta, C. Wu, A. G. Azzolini, D. Dzhulgakov, A. Mallevich, I. Cherniavskii, Y. Lu, R. Krishnamoorthi, A. Yu, V. Kondratenko, S. Pereira, X. Chen, W. Chen, V. Rao, B. Jia, L. Xiong, and M. Smelyanskiy, "Deep Learning Recommendation Model for Personalization and Recommendation Systems," arXiv preprint arXiv:1906.00091, May. 2019.
9 H. Wang, Z. Shen, S. Jiang, G. Sun, and R. J. Zhang, "User-based Collaborative Filtering Algorithm Design and Implementation," in Journal of Physics: Conference Series, Changsha, China, vol. 1757, no. 1, p. 012168, 2021.   DOI
10 P. Covington, J. Adams, and E. Sargin, "Deep Neural Networks for YouTube Recommendations," in Proceedings of the 10th ACM conference on recommender systems, New York: NY, USA, pp. 191-198, 2016.
11 The Concept and Application of the Recommended Algorithm and the Patterns of Development [Internet]. Available: https://www.kocca.kr/trend/vol20/sub/s21.html.
12 The Ethical and Privacy Issues of Recommendation Engines on M edia Platform s [Internet]. Available: https://towardsdatascience.com/the-ethical-and-privacy-issues-of-recommendation-engines-on-media-platforms-9bea7bcb0abc.
13 How Netflix Works. electronics [Internet]. Available: https://electronics.howstuffworks.com/netflix2.htm.
14 Introduction to recommender systems [Internet]. Available: https://thingsolver.com/introduction-to-recommender-systems/.
15 Recommend Deep Learning Personalization [Internet]. Available:https://medium.com/daangn/%EB%94%A5%EB%9F%AC%EB%8B%9D-%EA%B0%9C%EC%9D%B8%ED%99%94-%EC%B6%94%EC%B2%9C-1eda682c2e8c.
16 Y. Koren, R. Bell, and C. Volinsky, "Matrix Factorization Techniques for Recommender Systems," Computer, vol. 42, no. 8, pp. 30-37, Aug. 2009.
17 Recommendation system using knowledge graph [Internet]. Available:https://zzaebok.github.io/knowledge_graph/recommender_syste/KG_recommend/.
18 B. Sarwar, G. Karypis, J. Konstan, and J. Riedl, "Item-based collaborative filtering recommendation algorithms," in Proceedings of the 10th international conference on World Wide Web, New York: NY, USA. pp. 285-295, 2001.
19 What Content-Based Filtering is and Why You Should Use It [Internet]. Available: https://www.upwork.com/resources/what-is-content-based-filtering#:~:text=Content%2Dbased%filtering%is%a,them%to%a%user%profile.
20 Homepage of the dogpresident [Internet]. Available: https://dogpre.com/.
21 Draw your own ROC Curve and Precision-Recall Curve [Internet]. Available: https://yangoos57.github.io/ml/data_viz/roc_curve_and_pe_re_curve/.
22 The Cold Start Problem for Recommender System [Internet]. Available: https://medium.com/@markmilankovich/the-cold-start-problem-for-recommender-systems-89a76505a7.