Improving a recommendation engine with transfer learning — Pierre Gutierrez (Dataiku Labs)

For online businesses, recommender systems are paramount. There is an increasing need to take into account all the user information to tailor the best product offer, tailored to each new user.

Part of that information is the content that the user actually sees: the visuals of the products. When it comes to products like luxury hotels, pictures of the room, the building or even the nearby beach can significantly impact users’ decision.

In this talk, we will describe how we improved an online vacation retailer recommender system by using the information in images. We’ll explain how to leverage open data and pre-trained deep learning models to derive information on user taste. We will use a transfer learning approach that enables companies to use state of the art machine learning methods without needing deep learning expertise.


Pierre Gutierrez is a lead data scientist at Dataiku Labs in Paris, France. In the past few years, he has been working on state of the art Data Science and Machine learning problems in a large variety of sectors such as e-business, retail, insurance, or telcos. He has experience in topics such as fraud detection, bot detection, recommender systems, or churn prediction. Pierre has a pragmatic approach to data science and strongly believes in the power of transfer learning in image, text and AI.