Use of Hybrid Methods in Making E-commerce Product Recommendation Systems to Overcome Cold Start Problems

Budi Santosa, Muhamad Azam Fuadi, Mangaras Yanu Florestiyanto, Vynska Amalia Permadi, Wilis Kaswidjanti

Abstract


The large number of users and the items offered in e-commerce make it difficult for buyers to choose the right items and sellers to offer their items to the right buyers. To overcome this problem, a system that can offer and recommend goods automatically, namely a recommendation system is needed. One of the most popular methods used to create a recommendation system is collaborative filtering, the recommendations are created based on similarities in user behavior. Unfortunately, this method has a weakness, namely cold start, where the recommendations will be inaccurate on data that has a lot of new users and items due to minimal historical data regarding user behavior. This problem will be tried to be solved in this study using a hybrid method, where this method combines more than 1 method to create a list of recommendations so that it will cover the shortcomings of each method. This study uses Amazon's e-commerce product and transaction data. The use of the hybrid method in this study can overcome the cold start problem by using switching and mixed methods, by not using the collaborative filtering model on new user recommendations or users who have little interaction. New users will receive recommendations based on the combination of popularity-based and content-based filtering models. This can be seen from the Mean Absolute Error (MAE) value of the model, where the MAE value for the data with a minimum user has at least 3 times rating is 0.566883, for the minimum 7 times, the MAE value is smaller, 0.487553.

Keywords


E-Commerce; Recommendation System; Hybrid; Collaborative Filtering; Cold Start

Full Text:

Link Download

References


Aan, A., & Permana, J. (2019). Usability Testing Pada Website E-Commerce Menggunakan Metode System Usability Scale (SUS) (Studi Kasus : Umkmbuleleng.Com). 8(2), 149–158.

Arvianti, Q. R., Baizal, Z. K. A., & Tarwidi, D. (2019). Tourism Recommender System Using Item-Based Hybrid Clustering Method (Case Study : Bandung Raya Region). Journal of Data Science and Its Applications, 2(2), 95–101. https://doi.org/10.34818/JDSA.2019.2.35

Badriyah, T., Wijayanto, E. T., Syarif, I., & Kristalina, P. (2017). A hybrid recommendation system for E-commerce based on product description and user profile. 7th International Conference on Innovative Computing Technology, INTECH 2017, Intech, 95–100. https://doi.org/10.1109/INTECH.2017.8102435

Bilge, A., Kaleli, C., Yakut, I., Gunes, I., & Polat, H. (2013). A survey of privacy-preserving collaborative filtering schemes. International Journal of Software Engineering and Knowledge Engineering, 23(8), 1085–1108. https://doi.org/10.1142/S0218194013500320

Bourkoukou, O., & Achbarou, O. (2018). Weighting based approach for learning resources recommendations. International Journal on Informatics Visualization, 2(3), 104–109. https://doi.org/10.30630/joiv.2.3.124

B.Thorat, P., M. Goudar, R., & Barve, S. (2015). Survey on Collaborative Filtering, Content-based Filtering and Hybrid Recommendation System. International Journal of Computer Applications, 110(4), 31–36. https://doi.org/10.5120/19308-0760

Cacheda, F., Carneiro, V., Fernández, D., & Formoso, V. (2011). Comparison of collaborative filtering algorithms: Limitations of current techniques and proposals for scalable, high-performance recommender systems. ACM Transactions on the Web, 5(1). https://doi.org/10.1145/1921591.1921593

Cui, B.-B. (2017). Design and Implementation of Movie Recommendation System Based on Knn Collaborative Filtering Algorithm. ITM Web of Conferences, 12, 04008. https://doi.org/10.1051/itmconf/20171204008

Geetha, G., Safa, M., Fancy, C., & Saranya, D. (2018). A Hybrid Approach using Collaborative filtering and Content based Filtering for Recommender System. Journal of Physics: Conference Series, 1000(1). https://doi.org/10.1088/1742-6596/1000/1/012101

Guo, B., Xu, S., Liu, D., Niu, L., Tan, F., & Zhang, Y. (2017). Collaborative filtering recommendation model with user similarity filling. Proceedings of 2017 IEEE 3rd Information Technology and Mechatronics Engineering Conference, ITOEC 2017, 2017-Janua, 1151–1154. https://doi.org/10.1109/ITOEC.2017.8122536

Guo, G. (2013). Improving the performance of recommender systems by alleviating the data sparsity and cold start problems. IJCAI International Joint Conference on Artificial Intelligence, 3217–3218.

Knijnenburg, B. P., Willemsen, M. C., Gantner, Z., Soncu, H., & Newell, C. (2012). Explaining the user experience of recommender systems. User Modeling and User-Adapted Interaction, 22(4–5), 441–504. https://doi.org/10.1007/s11257-011-9118-4

Kusuma, H. S., & Musdholifah, A. (2021). Recommendation System for Thesis Topics Using Content-based Filtering. IJCCS (Indonesian Journal of Computing and Cybernetics Systems), 15(1), 65. https://doi.org/10.22146/ijccs.62716

Larasati, F. B. A., & Februariyanti, H. (2021). Sistem Rekomendasi Product Emina Cosmetics Dengan Menggunakan Metode Content-Based Filtering. 4(1).

Mobasher, B. (2007). Data mining for Web personalization. Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 4321 LNCS, 90–135. https://doi.org/10.1007/978-3-540-72079-9_3

Ni, J., Li, J., & McAuley, J. (2020). Justifying recommendations using distantly-labeled reviews and fine-grained aspects. EMNLP-IJCNLP 2019 - 2019 Conference on Empirical Methods in Natural Language Processing and 9th International Joint Conference on Natural Language Processing, Proceedings of the Conference, 188–197. https://doi.org/10.18653/v1/d19-1018

Noviansyah, M. R., Suharso, W., Chandranegara, D. R., Azmi, M. S., & Hermawan, M. (2019). Sistem Pendukung Keputusan Pemilihan Laptop Pada E-Commerce Menggunakan Metode Weighted Product. Prosiding SENTRA (Seminar Teknologi Dan Rekayasa), 0(5), 43–53. http://research-report.umm.ac.id/index.php/sentra/article/view/3025

Parwita, W. G. S. (2019). Pengujian Akurasi Sistem Rekomendasi Berbasis Content-Based Filtering. Informatika Mulawarman : Jurnal Ilmiah Ilmu Komputer, 14(1), 27. https://doi.org/10.30872/jim.v14i1.1272

Prasetya, C. S. D. (2017). Sistem Rekomendasi Pada E-Commerce Menggunakan K-Nearest Neighbor. Jurnal Teknologi Informasi Dan Ilmu Komputer, 4(3), 194. https://doi.org/10.25126/jtiik.201743392

Suka Parwita, W. G., Prami Swari, M. H., & Welda, W. (2018). Perancangan Sistem Rekomendasi Dokumen Dengan Pendekatan Content-Based Filtering. Computer Engineering, Science and System Journal, 3(1), 65. https://doi.org/10.24114/cess.v3i1.7855

Theodorus, A., & Budiyanto Setyohadi, D. (2016). User-Based Collaborative Filtering Dengan Memanfaatkan Pearson- Correlation Untuk Mencari Neighbors Terdekat Dalam Sistem Rekomendasi. Thesis Magister Teknologi Informasi Universitas Atma Jaya Yogyakarta, 1–6. http://e-journal.uajy.ac.id/8924/

Veras De Sena Rosa, R. E., Guimarães, F. A. S., Mendonça, R. D. S., & Lucena, V. F. de. (2020). Improving Prediction Accuracy in Neighborhood-Based Collaborative Filtering by Using Local Similarity. IEEE Access, 8, 142795–142809. https://doi.org/10.1109/ACCESS.2020.3013733

Wang, C. D., Deng, Z. H., Lai, J. H., & Yu, P. S. (2019). Serendipitous recommendation in e-commerce using innovator-based collaborative filtering. IEEE Transactions on Cybernetics, 49(7), 2678–2692. https://doi.org/10.1109/TCYB.2018.2841924

Zarei, M. R., & Moosavi, M. R. (2019). A Memory-Based Collaborative Filtering Recommender System Using Social Ties. 4th International Conference on Pattern Recognition and Image Analysis, IPRIA 2019, 263–267. https://doi.org/10.1109/PRIA.2019.8786023

Zhou, W., Li, R., & Liu, W. (2020). Collaborative Filtering Recommendation Algorithm based on Improved Similarity. Proceedings of 2020 IEEE 5th Information Technology and Mechatronics Engineering Conference, ITOEC 2020, Itoec, 321–324. https://doi.org/10.1109/ITOEC49072.2020.9141788




DOI: http://dx.doi.org/10.35671/telematika.v16i1.2080

Refbacks

  • There are currently no refbacks.


 



Indexed by:

   

Telematika
ISSN: 2442-4528 (online) | ISSN: 1979-925X (print)
Published by : Universitas Amikom Purwokerto
Jl. Let. Jend. POL SUMARTO Watumas, Purwonegoro - Purwokerto, Indonesia


Creative Commons License This work is licensed under a Creative Commons Attribution 4.0 International License .