# ProductNet: a Collection of High-Quality Datasets for Product Representation Learning Source: [ProductNet: a Collection of High-Quality Datasets for Product Representation Learning]() Collection index: [llms.txt]() Data updated: 2025-10-27T05:57:48.173Z Authors: Chu Wang, Lei Tang, Yang Lu, Shujun Bian, Hirohisa Fujita, Da Zhang, Zuohua Zhang, Yongning Wu Published: 2019-04-18T23:17:07.000Z ## 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. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. ## Original sources and implementation [Original paper]() [PDF]()