Presented By O’Reilly and Cloudera
Make Data Work
21–22 May 2018: Training
22–24 May 2018: Tutorials & Conference
London, UK

Deep learning for recommender systems

12:0512:45 Wednesday, 23 May 2018
Data science and machine learning
Location: Capital Suite 13 Level: Intermediate
Secondary topics:  E-commerce and Retail, Media, Advertising, Entertainment
Average rating: ****.
(4.43, 7 ratings)

Who is this presentation for?

  • Data scientists and machine learning engineers

Prerequisite knowledge

  • A basic understanding of machine learning and recommendations (useful but not required)

What you'll learn

  • Explore the cutting edge in deep learning applied to recommendation and personalization problems

Description

In the last few years, deep learning has achieved significant success in a wide range of domains, including computer vision, artificial intelligence, speech, NLP, and reinforcement learning. However, deep learning in recommender systems has, until recently, received relatively little attention. Nick Pentreath explores recent advances in this area in both research and practice.

Nick explains how deep learning can be applied to recommendation settings, architectures for handling contextual data, side information, and time-based models, compares deep learning approaches to other cutting-edge contextual recommendation models, and explores scalability issues and model serving challenges.

Photo of Nick Pentreath

Nick Pentreath

IBM

Nick Pentreath is a principal engineer in IBM’s Cognitive Open Technology Group, where he works on machine learning. Previously, he cofounded Graphflow, a machine learning startup focused on recommendations. He has also worked at Goldman Sachs, Cognitive Match, and Mxit. He is a committer and PMC member of the Apache Spark project and author of Machine Learning with Spark. Nick is passionate about combining commercial focus with machine learning and cutting-edge technology to build intelligent systems that learn from data to add business value.