Mô hình hệ thống khuyến nghị sản phẩm trong kinh doanh trực tuyến dựa vào khai thác dữ liệu phi cấu trúc

Tuấn Lê Triệu; Hoàn Phạm Minh

doi:10.24311/jabes/2022.33.12.2

Authors

Tuấn Lê Triệu Trường Đại học Công nghệ thông tin và Truyền thông, Đại học Thái Nguyên Author
Hoàn Phạm Minh Trường Đại học Kinh tế Quốc dân Author

DOI:

https://doi.org/10.24311/jabes/2022.33.12.2

Keywords:

Online Recommendation System, Virtual rating, Real rating, Unstructured data mining

Abstract

Unstructured data generated by customers on e-commerce websites become an important matter in research and development of online recommendation system. It’s assisting for online customers in making purchasing decisions. These types of data are providing new research directions for solving the challenges of traditional recommendation systems. The study proposes a model of an online recommendation system based on exploiting customers' comments. The system consists of two phases, the first is the unstructured data mining process and the second phase implements product recommendations according to the collaborative filtering model approach. Comment classification results integrated with the recommendation module to enhance information to users before they are making product selection decisions and overcome the problem of the new users. Therefore, the study proposes a way to build a virtual rating from the sentiment classification score instead of the real rating to overcome the problem of sparse data.

References

Abbasi-Moud, Z., Vahdat-Nejad, H., & Sadri, J. (2021). Tourism recommendation system based on semantic clustering and sentiment analysis. Expert Systems with Applications, 167(2–3), 114324. doi: 10.1016/j.eswa.2020.114324

Al-Ghuribi, S. M., & Noah, S. A. M. (2019). Multi-criteria review-based recommender system–the state of the art. IEEE Access, 7, 169446-169468. doi: 10.1109/ACCESS.2019.2954861

Al-Bakri, N. F., & Hashim, S. H. (2018). Reducing data sparsity in recommender systems. Al-Nahrain Journal of Science, 21(2), 138–147.

Ambrish, G., Ganesh, B., Ganesh, A., Srinivas, C., Dhanraj and Mensinkal, K. (2022). Logistic regression technique for prediction of cardiovascular disease. Global Transitions Proceedings, 3(1), 127–130.

Anh, V. (2018). Underthesea Document. In Underthesea. Retrieved from https://underthesea.readthedocs.io.

Arroyo-Fernández, I., Méndez-Cruz, C.-F., Sierra, G., Torres-Moreno, J.-M., & Sidorov, G. (2019). Unsupervised sentence representations as word information series: Revisiting TF–IDF. Computer Speech & Language, 56, 107–129. doi: 10.1016/j.csl.2019.01.005

Asani, E., Vahdat-Nejad, H., & Sadri, J. (2021). Restaurant recommender system based on sentiment analysis. Machine Learning with Applications, 6, 100114. doi: 10.1016/j.mlwa.2021.100114

Baharudin, B., Lee, L. H., Khan, K., & Khan, A. (2010). A review of machine learning algorithms for text-documents classification. Journal of Advances in Information Technology, 1(1), 4–20.

Berrar, D., Lopes, P., & Dubitzky, W. (2019). Incorporating domain knowledge in machine learning for soccer outcome prediction. Machine Learning, 108(1), 97–126.

Cambria, E., Poria, S., Gelbukh, A., & Thelwall, M. (2017). Sentiment analysis is a big suitcase. IEEE Intelligent Systems, 32, 74–80. doi: 10.1109/MIS.2017.4531228

Chapman, P., Clinton, J., Kerber, R., Khabaza, T., Reinartz, T., Shearer, C., Wirth, R. (1999). Crisp-dm 1.0.: Step-by-step data mining guide. CRISP-DM consortium. Retrieved from http://www.crisp-dm.org

Chen, L., Chen, G., & Wang, F. (2015). Recommender systems based on user reviews: The state of the art. User Modeling and User-Adapted Interaction, 25(2), 99–154. doi: 10.1007/s11257-015-9155-5

Fang, X., & Zhan, J. (2015). Sentiment analysis using product review data. Journal of Big Data, 2(1), 5–19. doi: 10.1186/s40537-015-0015-2

Farida, K. (2016). A survey of e-commerce recommender systems. European Scientific Journal, 12(34), 76–89. doi: 10.19044/esj.2016.v12n34p75

Graff, M., Moctezuma, D., Miranda-Jiménez, S., & Tellez, E. S. (2022). A Python library for exploratory data analysis on twitter data based on tokens and aggregated origin–destination information. Computers & Geosciences, 159, 105012. doi: 10.1016/j.cageo.2021.105012

Hoàng Thị Hà, & Ngô Nguyễn Thức. (2021). Một số phương pháp gợi ý và ứng dụng trong thương mại điện tử. Tạp chí khoa học nông nghiệp Việt Nam, 19(4), 520–534.

He, P., Zhu, J., He, S., Li, J., & Lyu, M. R. (2016). An evaluation study on log parsing and its use in log mining. Paper presented at the 2016 46th Annual IEEE/IFIP International Conference on Dependable Systems and Networks (DSN).

Heilig, L., Stahlbock, R., & Voß, S. (2020). From digitalization to data-driven decision making in container terminals. In J. W. Böse (Ed.), Handbook of Terminal Planning (pp. 125–154). Cham: Springer International Publishing.

Herlocker, J., Konstan, J., & Riedl, J. (2002). An empirical analysis of design choices in neighborhood-based collaborative filtering algorithms. Information Retrieval, 5, 287–310. doi: 10.1023/A:1020443909834

Hussien, F. T. A., Rahma, A. M. S., & Wahab, H. B. A. (2021). Recommendation Systems For E-commerce Systems An Overview. Paper presented at the Journal of Physics: Conference Series.

Juan, W., Yue-xin, L., & Chun-ying, W. (2019). Survey of recommendation based on collaborative filtering. Journal of Physics: Conference Series, 1314(1), 012078. doi: 10.1088/1742-6596/1314/1/012078

Kulkarni, A., Chong, D., & Batarseh, F. A. (2020). 5 - Foundations of data imbalance and solutions for a data democracy. In F. A. Batarseh & R. Yang (Eds.), Data Democracy (pp. 83–106). Academic Press.

Leung, C. W., Chan, S. C., & Chung, F.-l. (2006). Integrating collaborative filtering and sentiment analysis: A rating inference approach. Paper presented at the Proceedings of the ECAI 2006 workshop on recommender systems.

Lê Thị Minh Nguyện. (2019). Text classification based on support vector machine. Tạp chí Khoa học Đại học Đà Lạt, 9(2), 3–19.

Liu, H., Wang, Y., Peng, Q., Wu, F., Gan, L., Pan, L., & Jiao, P. J. N. (2020). Hybrid neural recommendation with joint deep representation learning of ratings and reviews. Neurocomputing, 374, 77–85.

Liu, Z., Luo, C., & Liu, G. (2017). Study on inductive effect of product information label on technology: An empirical based on the energy efficiency labeling system in China. Science & Technology Progress and Policy, 34(24), 18–24.

Mee, A., Homapour, E., Chiclana, F., & Engel, O. (2021). Sentiment analysis using TF–IDF weighting of UK MPs’ tweets on Brexit. Knowledge-Based Systems, 228(4), 107238. doi: 10.1016/j.knosys.2021.107238

Miao, X., Gao, Y., Chen, G., Cui, H., Guo, C., & Pan, W. (2016). SI2P: A restaurant recommendation system using preference queries over incomplete information. Proceedings of the VLDB Endowment, 9(13), 1509–1512. doi: 10.14778/3007263.3007296

Nguyễn Thái Nghe. (2016). Hệ thống gợi ý: Kỹ thuật và ứng dụng (trang 21–47). Cần Thơ: NXB Đại học Cần Thơ.

Omary, Z., & Mtenzi, F. (2010). Machine learning approach to identifying the dataset threshold for the performance estimators in supervised learning. International Journal for Infonomics, 3(3), 314–325.

Papadakis, H., Panagiotakis, C., & Fragopoulou, P. (2017). SCoR: A synthetic coordinate based recommender system. Expert Systems with Applications, 79, 8–19. doi: 10.1016/j.eswa.2017.02.025

Phu, V. N., Chau, V. T. N., Tran, V. T. N., & Dat, N. D. (2018). A Vietnamese adjective emotion dictionary based on exploitation of Vietnamese language characteristics. Artificial Intelligence Review, 50(1), 93–159. doi: 10.1007/s10462-017-9538-6

Thái Kim Phụng, Nguyễn An Tế, & Trần Thị Thu Hà. (2020). Hệ thống hỗ trợ đánh giá và khuyến nghị dịch vụ du lịch dựa trên khai thác ý kiến khách hàng trực tuyến. Tạp chí Khoa học và Công nghệ, 46, 175–189.

Tran, T. N. T., Atas, M., Felfernig, A., & Stettinger, M. (2018). An overview of recommender systems in the healthy food domain. Journal of Intelligent Information Systems, 50(3), 501–526. doi: 10.1007/s10844-017-0469-0

Pu, P., Chen, L., Hu, R. (2012). Evaluating recommender systems from the user’s perspective: Survey of the state of the art. User Modeling and User-Adapted Interaction, 22(4–5), 317–355.

Regina, I. A., & Sengottuvelan, P. (2021). Analysis of sentiments in movie reviews using supervised machine learning technique. Paper presented at the 2021 4th International Conference on Computing and Communications Technologies (ICCCT).

Resnick, P., Iacovou, N., Suchak, M., Bergstrom, P., & Riedl, J. (1994). Grouplens: An open architecture for collaborative filtering of netnews. Paper presented at the Proceedings of the 1994 ACM conference on Computer supported cooperative work.

Salzberg, S. L. (1994). Programs for Machine Learning. Kluwer Academic Publishers.

Sasikala, P., & Lourdusamy, M. I. S. (2018). Sentiment analysis and prediction of online reviews with empty ratings. International Journal of Applied Engineering Research, 13, 11525–11531.

Seo, Y.-D., Lee, E., & Kim, Y.-G. (2020). Video on demand recommender system for internet protocol television service based on explicit information fusion. Expert Systems with Applications, 143, 113045. doi: 10.1016/j.eswa.2019.113045

Sharma, D. K., Lohana, S., Arora, S., Dixit, A., Tiwari, M., & Tiwari, T. (2022a). E-commerce product comparison portal for classification of customer data based on data mining. Materials Today: Proceedings, 51, 166–171. doi: 10.1016/j.matpr.2021.05.068

Sharma, M., Mittal, R., Bharati, A., Saxena, D., & Singh, A. (2022b). A survey and classification on recommendation systems. Conference: 2nd International Conference on Big Data, Machine Learning and Applications.

Su, X., & Khoshgoftaar, T. M. (2009). A survey of collaborative filtering techniques. Advances in Artificial Intelligence, 2009(2), 2–21.

Trần Nguyễn Minh Thư, & Phạm Xuân Hiền. (2016). Các phương pháp đánh giá hệ thống gợi ý. Tạp chí khoa học Trường Đại học Cần Thơ, 42, 18–27.

Tuan, L. T., & Hoan, P. M. (2021). Method of content classification based on supervised machine learning in online comment mining of customer. Paper presented at the Socio-Economic and Environmental Issues in Development, pp. 823–832, National Economics University.

Lê Triệu Tuấn, & Đàm Thị Phương Thảo. (2022). Phương pháp phân loại dữ liệu bình luận của khách hàng trực tuyến Việt Nam dựa vào học máy có giám sát. Khoa học & Công nghệ, 58(1), 49–52.

Ziani, A., Azizi, N., Schwab, D., Aldwairi, M., Chekkai, N., Zenakhra, D., & Cheriguene, S. (2017). Recommender System Through Sentiment Analysis. Paper presented at 2nd International Conference on Automatic Control, Telecommunications and Signals (ICATS), Algeria.