Deep Hierarchical Classification for Category Prediction in E -commerce System (2020)

Abstract: In e-commerce system, category prediction is to automatically predict categories of given texts. Different from traditional classification where there are no relations between classes, category prediction is reckoned as a standard hierarchical classification problem since categories are usually organized as a hierarchical tree. In this paper, we address hierarchical category prediction. We propose a Deep Hierarchical Classification framework, which incorporates the multi-scale hierarchical information in neural networks and introduces a representation sharing strategy according to the category tree. We also define a novel combined loss function to punish hierarchical prediction losses. The evaluation shows that the proposed approach outperforms existing approaches in accuracy.

## introduction category prediction (cp), which aims to recognize the intent categories of given texts, is regarded as one of the most fundamental machine learning tasks in e-commerce system @xcite . for example, this predicted category information will influence product ranking in search and recommendation system. different from the traditional classification @xcite cp is formally categorized as a hierarchical classification task since categories in most ecommerce websites are organized as a hierarchical tree (we consider the situation that the categories are organized as a hierarchical tree, but not a directed acyclic graph). figure 1 .(a) shows a simplified fragment of one category architecture. apart from cp, there are also many other tasks belonging to hierarchical classification, e.g., image classification shown in figure 1 .(b). for simplicity, most practical approaches ignore the relation information between classes (hereafter referred to as flat classification). these approaches are easily implemented, but disadvantage in accuracy @xcite . in academy, the hierarchical classification problem is not wellstudied as well @xcite . except these flat approaches, published studies are mainly divided into two directions: the local approaches, and the global approaches @xcite . the local approaches learn multiple independent classifiers, each classifier either for per node, or for per parent node or for per layer. taking the local approach for per layer as an example, for figure 1 .(b) it will train two independent classifiers for layer_1 and layer_leaf, respectively. the global approaches regard all none-root nodes as the classes to predict. only one classifier is trained for all these none-root classes. we argue that all these approaches either do not consider the hierarchical structure at all (i.e., the flat approaches), or take implicit or tiny consideration of the class hierarchy. the main challenges in hierarchical classification are at two aspects: hierarchical representation in classification model and hierarchical inconsistency in training process. hierarchical representation means researchers may select naive bayesian @xcite , support vector machine @xcite , and neural networks (jurgen schmidhuber, 2015) as their classification models. but the hierarchical information fails to be explicitly incorporated in these models. consequentially, it is hard for these models to learn the complex hierarchical information. the hierarchical inconsistency means ## deep hierarchical classification mathematically, the hierarchical classification task can be formulated as: given: in the dhc approach, we defines a neural network model 𝒩(𝜃) where 𝜃 are the parameters to be estimated. taking a three-layer hierarchical classification problem as an example, we show the dhc neural network in figure 2 . the neural network is composed of three parts: flat neural network (fnn), hierarchical embedding network (hen) and hierarchical loss network (hln). we will further discuss these three parts. ## conclusions in sum, we extensively address the two challenges (i.e., hierarchical representation and hierarchical inconsistency) in hierarchical classification and propose the dhc approach to solve these two problems. experiments both on text and image