Label-Guided Learning for Item Categorization in E-commerce

Abstract

Item categorization is an important application of text classification in e-commerce due to its impact on the online shopping experience of users. One class of text classification techniques that has gained attention recently is using the semantic information of the labels to guide the classification task. We have conducted a systematic investigation of the potential benefits of these methods on a real data set from Rakuten, a major e-commerce company in Japan. We found that using pre-trained word embeddings specialized to specific categories of items performed better than one obtained from all available categories despite the reduction in data set size. Furthermore, using a hyperbolic space to embed product labels that are organized in a hierarchical structure led to better performance compared to using a conventional Euclidean space embedding. These findings demonstrate how label-guided learning can improve item categorization systems in the e-commerce domain.