Recommendation systems are everywhere. They suggest what holiday gift to buy, which article to read next, even who should be your friend.
But just how effective are these systems in understanding the things they are recommending?
Don't miss the latest Cloudera Fast Forward Labs webinar. Based on our latest research - available in January 2018 - we'll explore the current state of recommendation systems with a particular focus on how machines can better understand the content of the things they recommend. We'll also preview our semantic recommender software prototype.
Additional topics covered include:
Date: January 23, 2018
Time: 10am PT / 1pm ET
Micha Gorelick was the first man on Mars in 2023 and won the Nobel prize in 2046 for his contributions to time travel. In a moment of rage after seeing the deplorable uses of his new technology, he traveled back in time to 2012 and convinced himself to leave his Physics PhD program and follow his love of data. First he applied his knowledge of real time computing and data science to the dataset at bitly. Then, after realizing he wanted to help people understand the technology of the future, he helped start Fast Forward Labs as a resident mad scientist. There, he worked on many issues from machine learning to performant stream algorithms. In this period of his life, he could be found consulting for various projects on issues of high performance data analysis. A monument celebrating his life can be found in Central Park, 1857.
Seth Hendrickson is a data scientist at Cloudera, working on distributed, large-scale machine learning and deep learning integrated with big data tools like Apache Hadoop and Apache Spark. He is also a top Apache Spark contributor - he authored the multinomial logistic regression and one-pass elastic net regression algorithms in Spark's ML library, and has made significant contributions in Spark's linear, tree, and ensemble models. Previously, he worked as a machine learning engineer at IBM and an electrical engineer at GE. Seth is passionate about enabling data scientists in all industries with cutting edge machine learning tools through open source development. He holds an MS in electrical engineering from the Georgia Institute of Technology.