Abstract: Shopify’s Search and Discovery team is responsible for generating recommendations for millions of merchants that span many industries and countries. Specifically, when we think about content-based recommendations, we deal with product descriptions that vary in length, cleanliness and even coherence. In this talk, we will explore:
- The challenges we face when building content-based recommendation systems at Shopify.
- How we generated high-quality product embeddings using Universal Sentence Encoder (USE).
- Why we chose USE over other popular options such as BERT
- How we scaled our approach using Ray Actor Pools to generate recommendations for over 350M products.
- The impact of launching this new model to millions of merchants.
Very familiar with Python, Limited familiarity needed for Tensorflow and Ray Actorpools
Bio: Chen is a Senior Data Science Manager at Shopify, where she leads the Discovery Experience data team. Chen has focused on building search and discovery products using machine learning techniques, experimenting and running A/B tests to improve and measure feature impact, and collaborating with cross-disciplinary teams. She enjoys building high impact data science teams, and providing technical and strategic leadership. Aside from day to day work, Chen is also interested in fairness in AI and has published research in this domain. Prior to joining Shopify, Chen obtained an M.Sc. in astrophysics from McGill University, where she discovered 30 radio pulsars by developing signal processing algorithms for telescope data.