Fine-Grained Product Categorization in E-commerce

E-commerce sites usually leverage taxonomies for better organizing products. The fine-grained categories, regarding the leaf categories in taxonomies, are defined by the most descriptive and specific words of products. Fine-grained product categorization remains challenging, due to blurred concepts of fine grained categories (i.e. multiple equivalent or synonymous categories), instable category vocabulary (i.e. the emerging new products and the evolving language habits), and lack of labelled data. To address these issues, we proposes a novel Neural Product Categorization model---NPC to identify fine-grained categories from the product content. NPC is equipped with a character-level convolutional embedding layer to learn the compositional word representations, and a spiral residual layer to extract the word context annotations capturing complex long range dependencies and structural information. To perform categorization beyond predefined categories, NPC categorizes a product by jointly recognizing categories from the product content and predicting categories from predefined category vocabularies. Furthermore, to avoid extensive human labors, NPC is able to adapt to weak labels, generated by mining the search logs, where the customers' behaviors naturally connect products with categories. Extensive experiments performed on a real e-commerce platform datasets illustrate the effectiveness of the proposed models.