Presented By O’Reilly and Cloudera
Make Data Work
September 11, 2018: Training & Tutorials
September 12–13, 2018: Keynotes & Sessions
New York, NY

Semantic recommendations

Shioulin Sam (Cloudera Fast Forward Labs)
11:20am–12:00pm Wednesday, 09/12/2018
Data science and machine learning
 Location: 1A 06/07 Level: Beginner
Secondary topics:  Deep Learning, Recommendation Systems
Average rating: ***..
(3.25, 4 ratings)

Who is this presentation for?

  • Everyone will find value in this session.

What you'll learn

  • Learn how classical recommendation systems work, their limitations, and why they break down in real-world applications
  • Explore multimodal embeddings and discover how they enable you to build better recommenders

Description

Recommendation systems are everywhere, and in data science, we often consider them to be a solved problem. However, a typical recommendation system approaches breakdown when it encounters real-world data and businesses. Shioulin Sam explores the limitations of classical recommendation approaches and explains how multimodal embeddings, an emerging technique from deep learning research, enable you to build a better system.

Classical recommendation approaches do not understandd the content of the items they are recommending. In addition, they suffer from the cold start problem and are unable to create recommendations for items or users they haven’t seen before. Multimodal embeddings allow you to uncover how semantic content of items relates to users preferences. This new capability can help solve common recommendation pitfalls, such as the cold start problem, and open up new product possibilities.

Photo of Shioulin Sam

Shioulin Sam

Cloudera Fast Forward Labs

Shioulin Sam is a research engineer at Cloudera Fast Forward Labs, where she bridges academic research in machine learning with industrial applications. Previously, she managed a portfolio of early stage ventures focusing on women-led startups and public market investments and worked in the investment management industry designing quantitative trading strategies. She holds a PhD in electrical engineering and computer science from the Massachusetts Institute of Technology.