Product Characterisation towards Personalisation: Learning Attributes from Unstructured Data to Recommend Fashion Products
This addresses personalization and demand forecasting problems for large fashion e-commerce retailers like ASOS, but it is incremental as it builds on existing hybrid recommender systems.
The paper tackles the challenge of lacking consistent product information in e-commerce by developing a method to learn attributes from unstructured data, which is then used to improve fashion product recommendations in a hybrid system.
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.