Hybrid deep learning approach for product categorization in e-commerce.

Selling and purchasing products online is made possible by e-commerce. For both service providers and clients, organizing and looking for items is a tedious procedure. The items must be organized and labeled, which takes up a lot of time. Product categorization is the process of automatically predicting a product's catalog route based on a predetermined catalog hierarchy in which all categories are formulated. Knowing how to add your goods to the most relevant category on any marketplace, including Flipkart, and Amazon is crucial to its selling. Categorization is a lengthy process that takes extensive study on the platform which has been improved with different methodologies used in this work. Machine Learning (ML) and Deep Learning (DL) models are used to sort items into recognized categories. Using information such as the item's title and summary, this model can properly classify it in each classification. Random Forest (RF) outperformed the other ML models, such as SVM, KNN, and Naive Bayes (NB), with an f1-score of 97 percent and a macro average of 94 percent. BERT model fared the best among the DL models (LSTM, CNN, BERT, and Hybrid CNN - LSTM model) with an f1-score of 97 percent and a macro average of 88 percent. [ABSTRACT FROM AUTHOR]