Enhanced Attention-Based Multimodal Deep Learning for Product Categorization on E-Commerce Platform

Abstract

Labeling and classifying a large number of products is one of the key challenges that e-commerce managers face. Building an automatic model that can accurately classify products helps to optimize the consumer search experience and ensure that they can easily find the products that meet their needs. In this study, we propose an improved Multimodal Deep Learning Model, based on the attention mechanism. This model has the ability to significantly improve accuracy over both traditional Unimodal Deep Learning and Multimodal Deep Learning models. The accuracy of our proposed model reaches 91.18% in classifying 16 different product categories. Meanwhile, traditional Multimodal Deep Learning models only achieved a modest accuracy of 77.21%. This result not only improves the searchability and online shopping experience of consumers, but also makes a significant contribution to solving the challenge of product classification on e-commerce platforms.