# Product Characterisation towards Personalisation: Learning Attributes from Unstructured Data to Recommend Fashion Products

## TL;DR

This paper presents a method for extracting detailed product attributes from unstructured data to enhance personalization, demand forecasting, and product planning in fashion e-commerce.

## Contribution

It introduces a novel architecture and methodology for learning consistent product attributes from unstructured data at scale, improving recommendation systems.

## Key findings

- Enhanced product attribute extraction accuracy
- Improved recommendation relevance
- Better demand forecasting capabilities

## Abstract

In this paper, we describe a solution to tackle a common set of challenges in e-commerce, which arise from the fact that new products are continually being added to the catalogue. The challenges involve properly personalising the customer experience, forecasting demand and planning the product range. We argue that the foundational piece to solve all of these problems is having consistent and detailed information about each product, information that is rarely available or consistent given the multitude of suppliers and types of products. We describe in detail the architecture and methodology implemented at ASOS, one of the world's largest fashion e-commerce retailers, to tackle this problem. We then show how this quantitative understanding of the products can be leveraged to improve recommendations in a hybrid recommender system approach.