Building a Fashion Recommender System from Learned Embeddings


In the workshop, I will walk you through building a recommender system from learned embeddings in a fashion e-commerce platform. You will learn how to collect and represent the users' interaction data, building a model to generate embeddings and how to use approximate nearest neighbour algorithms to build a similarity model. You will also learn how to tackle the cold start problem and finally the techniques whereby you can evaluate the recommendation systems.


Seyed Saeid Masoumzadeh is a senior data scientist at Lyst, a world largest fashion search platform. He has extensive experience in researching and developing Machine Learning, Deep Learning and NLP, and delivering them into production. Saeid is also the Co-founder of Cyra, a smart AI-based recruiting assistant, backed by “Entrepreneur First”, an international Talent Investor. Saeid has received his master degree in artificial intelligence and his PhD in computer science from the University of Vienna. He has published several peer-reviewed papers in reputed international journals and conferences.

Open Data Science




Open Data Science
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Cambridge, MA 02142

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