# Product Classification in E-Commerce using Distributional Semantics

arXiv: https://arxiv.org/abs/1606.06083 · HTML: https://mlsanity.com/paper/1606.06083/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Jun 2016.

## Built on by (most cited first, 9 of 9)

- [Contrastive language and vision learning of general fashion concepts](https://mlsanity.com/paper/2204.03972/) (2022)
- [Did AI get more negative recently?](https://mlsanity.com/paper/2202.13610/) (2022)
- [Unsupervised Contextualized Document Representation](https://mlsanity.com/paper/2109.10509/) (2021)
- [Cross-Platform E-Commerce Product Categorization and Recategorization: A Multimodal Hierarchical Classification Approach](https://mlsanity.com/paper/2508.20013/) (2025)
- [An Exploratory Study on Utilising the Web of Linked Data for Product Data Mining](https://mlsanity.com/paper/2109.01411/) (2021)
- [Ranking hierarchical multi-label classification results with mLPRs](https://mlsanity.com/paper/2205.07833/) (2022)
- [(Vector) Space is Not the Final Frontier: Product Search as Program Synthesis](https://mlsanity.com/paper/2304.11473/) (2023)
- [Towards Better Query Classification with Multi-Expert Knowledge Condensation in JD Ads Search](https://mlsanity.com/paper/2308.01098/) (2023)
- [Classification of retail products: From probabilistic ranking to neural networks](https://mlsanity.com/paper/2312.07482/) (2023)

## Abstract (arXiv)

Product classification is the task of automatically predicting a taxonomy path for a product in a predefined taxonomy hierarchy given a textual product description or title. For efficient product classification we require a suitable representation for a document (the textual description of a product) feature vector and efficient and fast algorithms for prediction. To address the above challenges, we propose a new distributional semantics representation for document vector formation. We also develop a new two-level ensemble approach utilizing (with respect to the taxonomy tree) a path-wise, node-wise and depth-wise classifiers for error reduction in the final product classification. Our experiments show the effectiveness of the distributional representation and the ensemble approach on data sets from a leading e-commerce platform and achieve better results on various evaluation metrics compared to earlier approaches.