Rapid Classification and Analysis for E-Commerce Goods Based on Multitask Learning

Abstract

Today’s E-commerce is hot, while the categorization of goods cannot be handled better, especially to achieve the demand of multiple tasks. In this paper, we propose a multitask learning model based on a CNN in parallel with a BiLSTM optimized by an attention mechanism as a training network for E-commerce. The results showed that the fast classification task of E-commerce was performed using only 10% of the total number of products. The experimental results show that the accuracy of w-item2vec for product classification can be close to 50% with only 10% of the training data. Both models significantly outperform other models in terms of classification accuracy.