# Categorising Products in an Online Marketplace: An Ensemble Approach

arXiv: https://arxiv.org/abs/2304.13852 · HTML: https://mlsanity.com/paper/2304.13852/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 6 of its 21 references that are in the map.
- First posted on arXiv 26 Apr 2023.

## Builds on (most cited first, 6 of 6)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Effective Approaches to Attention-based Neural Machine Translation](https://mlsanity.com/paper/1508.04025/) (2015)
- [Encoding high-cardinality string categorical variables](https://mlsanity.com/paper/1907.01860/) (2019)
- [Classifier Chains: A Review and Perspectives](https://mlsanity.com/paper/1912.13405/) (2019)
- [Don't Classify, Translate: Multi-Level E-Commerce Product Categorization Via Machine Translation](https://mlsanity.com/paper/1812.05774/) (2018)
- [Handling Imbalanced Data: A Case Study for Binary Class Problems](https://mlsanity.com/paper/2010.04326/) (2020)

## Abstract (arXiv)

In recent years, product categorisation has been a common issue for E-commerce companies who have utilised machine learning to categorise their products automatically. In this study, we propose an ensemble approach, using a combination of different models to separately predict each product's category, subcategory, and colour before ultimately combining the resultant predictions for each product. With the aforementioned approach, we show that an average F1-score of 0.82 can be achieved using a combination of XGBoost and k-nearest neighbours to predict said features.