An NLP inspired deep recommender engine for out-of-catalog items
At Chrono24 we are dealing with the largest inventory of luxury watches worldwide, covering >10 million of watches. Many of these items are limited editions or in other ways unique. In this talk we want to recap the journey from using a collaborative filtering approach for our watch recommender system to a modern, custom deep learning approach. We show how we solved the problem to recommend unseen not-yet-in-catalog watches, by building an embedding model based on their attributes, using techniques from natural language processing.
Florentin Kristen is working as a Machine Learning Engineer at Chono24 GmbH since 2020. He is focused on using machine learning to build an exclusive relationships with the customers, provide a safe shopping experience and increase the marketplace quality overall. Florentin implements and deploys computer vision and NLP models, including image quality assessment, image classification and recommender systems. Before joining Chrono24 GmbH he finished his degree in Computer Science at KIT(Karlsruhe Institute of Technology) with a specialization on deep learning and computer vision.