Presented By
O’Reilly + Cloudera
Make Data Work
29 April–2 May 2019
London, UK

Deep learning for recommender systems

Oliver Gindele (Datatonic)
12:0512:45 Thursday, 2 May 2019
Data Science, Machine Learning & AI, Expo Hall
Location: Expo Hall (Capital Hall N24)
Average rating: ****.
(4.50, 6 ratings)

Who is this presentation for?

  • Data scientists and machine learning practitioners

Level

Intermediate

Prerequisite knowledge

  • A basic understanding of machine learning and deep learning
  • Familiarity with Python

What you'll learn

  • Learn how to implement deep learning models in TensorFlow

Description

Recommender systems are widely used by ecommerce and services companies worldwide to provide the most relevant items to their users. Over the past few years, deep learning has demonstrated breakthrough advances in image recognition and natural language processing.

Meanwhile, new approaches have been published that apply deep learning techniques to recommender systems, further expanding the use cases of neural networks. Some of these novel systems already display state-of-the-art performance and deliver high-quality recommendations. Compared to traditional models, deep learning solutions can provide a better understanding of user’s demands, item’s characteristics, and the historical interactions between them.

Oliver Gindele explains how to implement some of these novel models in the machine learning framework TensorFlow, starting from a collaborative filtering approach and extending that to more complex deep recommender systems. TensorFlow can do more than vision or translation. High-level APIs make model building and training painless; custom algorithms and specific loss functions are easily implemented; deep recommender systems work well on real data; and embeddings and hidden layers allow for many ways to improve a recommender system.

Photo of Oliver Gindele

Oliver Gindele

Datatonic

Oliver Gindele is head of machine learning at Datatonic. Oliver is passionate about using computers models to solve real-world problems. Working with clients in retail, finance, and telecommunications, he applies deep learning techniques to tackle some of the most challenging use cases in these industries. He studied materials science at ETH Zurich and holds a PhD in computational physics from UCL.