E-commerce Product Categorization Using Big Data Analytics

Year 2021, Volume: 1 Issue: 2, 1 - 8, 30.09.2021

Sedat Usluoğlu Deniz Kılınç Fatma Bozyiğit

Abstract

E-commerce platforms need to have a well-managed online product catalog to make products easily accessible. However, the organization of catalog and categorization of products can be time-consuming due to the large volume of product data in e-commerce. In this direction, our study aims to develop an accurate categorization of product data with the adoption of big data analytics. Accordingly, various machine learning algorithms (Support Vector Machine, Naive Bayes, and Stochastic Gradient Descent) were utilized to organize online catalogs from Spark MLLib. Performed classifiers were trained and tested on product catalog data collected from a fashion retailer in Turkey, Boyner Group, which combines cutting-edge digital services with a vast network of exciting stores.

Keywords

e-commerce platforms, product categorization, machine learning, big data analytic tools

References

• [1] Sila, I. (2013). Factors affecting the adoption of B2B e-commerce technologies. Electronic commerce research, 13(2), 199-236.
• [2] Umaashankar, V., & Prakash, A. (2019). Atlas: A Dataset and Benchmark for E-commerce Clothing Product Categorization. arXiv preprint arXiv:1908.08984.
• [3] Boyner. (2021, May, 15). Internetin boyner’i online alışverisin adresi [Online]. Available: https://www.boynergrup.com/en
• [4] Shen, D., Ruvini, J. D., Somaiya, M., & Sundaresan, N. (2011, October). Item categorization in the e-commerce domain. In Proceedings of the 20th ACM international conference on Information and knowledge management (pp. 1921-1924).
• [5] Mathivanan, N. M. N., MdGhani, N. A., & Janor, R. M. (2019). Performance analysis of supervised learning models for product title classification. IAES International Journal of Artificial Intelligence, 8(3), 228.
• [6] Zahavy, T., Magnani, A., Krishnan, A., & Mannor, S. (2016). Is a picture worth a thousand words? a deep multi-modal fusion architecture for product classification in e-commerce. arXiv preprint arXiv:1611.09534.
• [7] Manchusha, K. N. R., & Renukadevi, P. Recursive Product Catalog Pattern Matching and Learning for Categorization of Products in Commercial Portal.
• [8] Minelli, M., Chambers, M., & Dhiraj, A. (2013). Big data, big analytics: emerging business intelligence and analytic trends for today's businesses (Vol. 578). John Wiley & Sons.
• [9] Akter, S., & Wamba, S. F. (2016). Big data analytics in E-commerce: a systematic review and agenda for future research. Electronic Markets, 26(2), 173-194.
• [10] Salloum, S., Dautov, R., Chen, X., Peng, P. X., & Huang, J. Z. (2016). Big data analytics on Apache Spark. International Journal of Data Science and Analytics, 1(3), 145-164.
• [11] Meng, X., Bradley, J., Yavuz, B., Sparks, E., Venkataraman, S., Liu, D., ... & Talwalkar, A. (2016). Mllib: Machine learning in apache spark. The Journal of Machine Learning Research, 17(1), 1235-1241.
• [12] Kılınç, D. (2019). A spark‐based big data analysis framework for real‐time sentiment prediction on streaming data. Software: Practice and Experience, 49(9), 1352-1364.
• [13] Bozyiğit, A., Utku, S., & Nasibov, E. (2021). Cyberbullying detection: Utilizing social media features. Expert Systems with Applications, 179, 115001.
• [14] Özçift, A., Kilinc, D., & Bozyigit, F. (2019). Application of grid search parameter optimized Bayesian logistic regression algorithm to detect cyberbullying in Turkish microblog data. Academic Platform Journal of Engineering and Science, 7(3), 355-361.