ProductNet: a Collection of High-Quality Datasets for Product Representation Learning
Abstract
ProductNet is a collection of high-quality product datasets for better product understanding. Motivated by ImageNet, ProductNet aims at supporting product representation learning by curating product datasets of high quality with properly chosen taxonomy. In this paper, the two goals of building high-quality product datasets and learning product representation support each other in an iterative fashion: the product embedding is obtained via a multi-modal deep neural network (master model) designed to leverage product image and catalog information; and in return, the embedding is utilized via active learning (local model) to vastly accelerate the annotation process. For the labeled data, the proposed master model yields high categorization accuracy (94.7% top-1 accuracy for 1240 classes), which can be used as search indices, partition keys, and input features for machine learning models. The product embedding, as well as the fined-tuned master model for a specific business task, can also be used for various transfer learning tasks.
Keywords
Cite
@article{arxiv.1904.09037,
title = {ProductNet: a Collection of High-Quality Datasets for Product Representation Learning},
author = {Chu Wang and Lei Tang and Yang Lu and Shujun Bian and Hirohisa Fujita and Da Zhang and Zuohua Zhang and Yongning Wu},
journal= {arXiv preprint arXiv:1904.09037},
year = {2019}
}