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Deep Learning for Recommender Systems
- Date: March 12, 2018
- Speaker: Marcel Kurovsk
- Affiliation: Big Data Scientist at inovex
Abstract
“People that bought X also bought Y”, “Discover Weekly”, “Top 10 for You” — online services personalized by recommendations are all around us.
Online giants like Amazon, Netflix and Spotify use Recommender Systems and Machine Learning to personalize content. This development is accompanied by another remarkable trend in learning machines: Deep Learning.
Almost daily we hear about the tremendous progress rumored to make humans obsolete in more and more domains: skin cancer detection, No-Limit-Hold’em Heads-Up Poker, or speech recognition (no, not yet another Alpha-Go example).
For his master thesis at inovex, Marcel Kurovski studied the application of Deep Learning for Recommender Systems. He developed a system for vehicle recommendation in TensorFlow based on data from a large German online market that extends Google’s Wide and Deep Learning approach. In his talk, Marcel will share his insights in this emerging research area and those of other big players.
Speaker bio
Marcel Kurovski, Big Data Scientist at inovex
Marcel specializes in deep learning and its application to recommender systems. He studied Industrial Engineering and Management at the Karlsruhe Institute of Technology (KIT) where he focused on machine learning, simulation and operations research.