Presented By O'Reilly and Cloudera
Make Data Work
Dec 4–5, 2017: Training
Dec 5–7, 2017: Tutorials & Conference

Deep learning for recommender systems

Tim Seears (Think Big, a Teradata company), Karthik Bharadwaj Thirumalai (Teradata)
1:30pm5:00pm Tuesday, December 5, 2017
Average rating: *....
(1.17, 6 ratings)

Who is this presentation for?

  • Data scientists and engineers

Prerequisite knowledge

  • Basic familiarity with TensorFlow training APIs and recommendation systems
  • A working knowledge of Python, linear algebra, and matrix operations

Materials or downloads needed in advance

  • A laptop with the ability to SSH into a server and a browser that can connect to Jupyter notebooks on servers installed (You will be provided with GPU-powered servers for use in training models with the Movie Lens dataset installed; if your your laptop has an NVIDIA GPU, you can download the Movie Lens dataset to work on locally.)

What you'll learn

  • Learn how to train deep learning models for categories, train weighted alternated least square models for exceptions, build models in Python using TensorFlow, test TensorFlow models, and visualize models with TensorBoard


Recommendation systems are increasingly important in our digital world, and many organizations are looking to machine learning to create recommendations that leverage their unique datasets to provide better results. Most applications have used techniques best described as wide learning: logistic regression, minhash, and weighted alternated least squares (WALS). As part of this trend, many leading digital companies are using deep learning to create recommendation models, simplifying feature engineering and resulting in improved performance.

Tim Seears and Karthik Bharadwaj Thirumalai explain how to apply deep learning to improve consumer recommendations by training neural nets to learn categories of interest using embeddings. They then demonstrate how to extend this with WALS matrix factorization to achieve wide and deep learning—a process which is now used in production for the Google Play Store.

Topics include:

  • Fundational concepts for wide and deep models
  • Deep learning models using neural nets with embeddings
  • Measuring model performance
  • Visualizing model behavior
  • Wide learning models using TensorFlow MLToolkit’s WALS matrix factorization
  • Deep and wide learning combining both techniques
  • Ideas for further applications of deep and wide learning
Photo of Tim Seears

Tim Seears

Think Big, a Teradata company

Tim Seears is area practice director for Asia-Pacific at Think Big, a Teradata company. Previously he was CTO of Big Data Partnership (acquired by Teradata in 2016), which he cofounded after a career spent in the space industry working on NASA’s Cassini orbiter mission at Saturn. Tim and his team established Big Data Partnership as a dominant thought leader throughout the European market, providing data science, data engineering, and big data architecture services to global enterprise customers.

Photo of Karthik Bharadwaj Thirumalai

Karthik Bharadwaj Thirumalai


Karthik Bharadwaj is a senior data scientist in the Data Science Center of Expertise at Teradata, where he provides analytic thought leadership and generating demand for Teradata products. Karthik has seven years of experience in working in the data management and analytics industry. Previously, he worked as a researcher at IBM Research to develop smarter transportation systems that predict traffic on the Singapore road network. Karthik holds a master’s degree from the National University of Singapore.

Comments on this page are now closed.


12/04/2017 7:16am +08

Yes, you are welcome. The notebooks are for your reference which you can try later.

Yong Boon Lim |
12/01/2017 5:59pm +08

Unfortunately I do not have a laptop. Can I still attend?