Multi-Label Product Categorization Using Multi-Modal Fusion Models
This addresses product categorization for e-commerce platforms, but it is incremental as it applies existing fusion techniques to a specific dataset.
The study tackled multi-label product categorization on Amazon by using a tri-modal late fusion model combining images, descriptions, and titles, improving the F1 score to 88.2% compared to single-modal baselines.
In this study, we investigated multi-modal approaches using images, descriptions, and titles to categorize e-commerce products on Amazon. Specifically, we examined late fusion models, where the modalities are fused at the decision level. Products were each assigned multiple labels, and the hierarchy in the labels were flattened and filtered. For our individual baseline models, we modified a CNN architecture to classify the description and title, and then modified Keras' ResNet-50 to classify the images, achieving $F_1$ scores of 77.0%, 82.7%, and 61.0%, respectively. In comparison, our tri-modal late fusion model can classify products more effectively than single modal models can, improving the $F_1$ score to 88.2%. Each modality complemented the shortcomings of the other modalities, demonstrating that increasing the number of modalities can be an effective method for improving the performance of multi-label classification problems.