Product Matching with Two-Branch Neural Network Embedding

Abstract

E-commerce platforms play a crucial role in facilitating transactions between buyers and sellers, with technological advancements significantly influencing consumer behaviors. Efficiently managing product catalogs is essential, particularly for identifying and matching products across various channels. This paper explores deep learning techniques for product matching, leveraging both text and image modalities to enhance the accuracy and efficiency of this process. We propose a novel approach using a branch neural network embedding space integrated with K-nearest neighbors (KNN), treating image and text as distinct modalities. For text embedding, we utilize pre-trained BERT and CharacterBERT models, while for image embedding, we employ EfficientNet. Our methodology incorporates the ArcFace loss function to enhance intra-class compactness and inter-class discrepancy, thereby improving classification performance. Our results demonstrate that integrating multimodal embeddings with advanced loss functions like ArcFace significantly enhances the performance of product matching systems. This approach offers valuable insights for developing robust e-commerce platforms.