The Hybrid Recommender System of the Indonesian Online Market Products using IMDb weight rating and TF-IDF

  • Muhammad Johari Universitas Amikom Yogyakarta
  • Arif Laksito Universitas Amikom Yogyakarta
Keywords: Recommender System, Indonesian Online Marketplace, Hybrid Filtering


Today, consumers are faced with an abundance of information on the internet; accordingly, it is hard for them to reach the vital information they need. One of the reasonable solutions in modern society is implementing information filtering. Some researchers implemented a recommender system as filtering to increase customers’ experience in social media and e-commerce. This research focuses on the combination of two methods in the recommender system, that is, demographic and content-based filtering, commonly it is called hybrid filtering. In this research, item products are collected using the data crawling method from the big three e-commerce in Indonesia (Shopee, Tokopedia, and Bukalapak). This experiment has been implemented in the web application using the Flask framework to generate products’ recommended items. This research employs the IMDb weight rating formula to get the best score lists and TF-IDF with Cosine similarity to create the similarity between products to produce related items.  


Download data is not yet available.


C. S. D. Prasetya, “Sistem Rekomendasi Pada E-Commerce Menggunakan K-Nearest Neighbor,” J. Teknol. Inf. dan Ilmu Komput., vol. 4, no. 3, p. 194, 2017.

E. Çano and M. Morisio, “Hybrid recommender systems: A systematic literature review,” Intell. Data Anal., vol. 21, no. 6, pp. 1487–1524, 2017.

X. Zhao, “A study on E-commerce recommender system based on big data,” 2019 IEEE 4th Int. Conf. Cloud Comput. Big Data Anal. ICCCBDA 2019, no. 1, pp. 222–226, 2019.

A. E. Wijaya and D. Alfian, “Sistem Rekomendasi Laptop Menggunakan Collaborative Filtering Dan Content-Based Filtering,” J. Comput. Bisnis, vol. 12, no. 1, pp. 11–27, 2018.

O. Bourkoukou and E. El Bachari, “Toward a hybrid recommender system for e-learning personalization based on data mining techniques,” Int. J. Informatics Vis., vol. 2, no. 4, pp. 271–278, 2018.

J. K. Tarus, Z. Niu, and D. Kalui, “A hybrid recommender system for e-learning based on context awareness and sequential pattern mining,” Soft Comput., vol. 22, no. 8, pp. 2449–2461, 2018.

M. E. B. H. Kbaier, H. Masri, and S. Krichen, “A personalized hybrid tourism recommender system,” Proc. IEEE/ACS Int. Conf. Comput. Syst. Appl. AICCSA, vol. 2017-Octob, pp. 244–250, 2018.

R. Logesh and V. Subramaniyaswamy, Exploring Hybrid Recommender Systems for Personalized Travel Applications, vol. 2018-Janua, no. July. Springer, Singapore, 2018.

F. A. Suharno and L. Listiyoko, “Aplikasi Berbasis Web dengan Metode Crawling sebagai Cara Pengumpulan Data untuk Mengambil Keputusan,” Semin. Nas. Rekayasa Teknol. Inf., no. November, pp. 105–109, 2018.

J. Eka Sembodo, E. Budi Setiawan, and Z. Abdurahman Baizal, “Data Crawling Otomatis pada Twitter,” in Indonesian Symposium on Computing, 2016, no. August, pp. 11–16.

A. Dwi Laksito, Kusrini, H. Sismoro, F. Rahmawati, and M. Yusa, “A Comparison Study of Search Strategy on Collecting Twitter Data for Drug Adverse Reaction,” Proc. - 2018 Int. Semin. Appl. Technol. Inf. Commun. Creat. Technol. Hum. Life, iSemantic 2018, pp. 356–360, 2018.

S. Raghavendra, Python Testing with Selenium. Apress, 2021.

A. Pajankar, Practical Python Data Visualization. Apress, Berkeley, CA, 2021.

Hanafi, N. Suryana, and A. S. B. H. Basari, “An understanding and approach solution for cold start problem associated with recommender system: A literature review,” J. Theor. Appl. Inf. Technol., vol. 96, no. 9, pp. 2677–2695, 2018.

S. Sfenrianto, M. H. Saragih, and B. Nugraha, “E-commerce recommender for usage bandwidth hotel,” Indones. J. Electr. Eng. Comput. Sci., vol. 9, no. 1, pp. 227–233, 2018.

M. Sridevi and R. Rajeswara Rao, “DECORS: A Simple and Efficient Demographic Collaborative Recommender System for Movie Recommendation,” Adv. Comput. Sci. Technol., vol. 10, no. 7, pp. 1969–1979, 2017.

“IMDb | Help - Weighted Average Ratings.” [Online]. Available:

G. Adomavicius and A. Tuzhilin, “Toward the next generation of recommender systems: A survey of the state-of-the-art and possible extensions,” IEEE Trans. Knowl. Data Eng., vol. 17, no. 6, pp. 734–749, 2005.

C. C. Aggarwal, Recommender Systems The Textbook. Switzerland: Springer International Publishing, 2016.

S. Qaiser and R. Ali, “Text Mining: Use of TF-IDF to Examine the Relevance of Words to Documents,” Int. J. Comput. Appl., vol. 181, no. 1, pp. 25–29, 2018.

J. Han, M. Kamber, and J. Pei, Data mining concepts and techniques, Third Edit. Waltham: Elsevier Inc, 2012.

P. Singh, Deploy Machine Learning Models to Production. Apress, 2021.

K. Relan, Building REST APIs with Flask. Apress, 2019.

How to Cite
Johari, M., & Laksito, A. (2021). The Hybrid Recommender System of the Indonesian Online Market Products using IMDb weight rating and TF-IDF. Jurnal RESTI (Rekayasa Sistem Dan Teknologi Informasi), 5(5), 977 - 983.
Artikel Teknologi Informasi