Adi Schwartz is a Data Scientist in the AutoML team at Outbrain. She develops machine learning infrastructure, as well as researches, develops, and deploys ML models. Adi is very curious and loves understanding data and how algorithms are implemented. After completing a bachelor's degree in Environmental Engineering and working in the field for many years, she has used her engineering knowledge to shift towards the data science world. When she's not working, Adi loves binge-watching, scuba diving, and trekking high and snowy mountains.
Kfar Saba, Israel
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FFM - Logistic Regression on Steroids
Data / AI / ML
Have you ever wondered how Recommender Systems work? How did the website you visited come to the conclusion that you were interested in sporting goods? What if you're a first-time user who hasn't visited the site before? How does the site determine what to recommend to you? One of the cutting-edge algorithms that can help recommender systems is the Field Aware Factorization Machine (FFM) algorithm. In this session, I'll take you on a journey through the history of recommendation algorithms, starting with Logistic Regression, moving on to Poly2 and Factorization Machines (FM), and eventually concluding with FFM. By the end of this talk, you'll have a greater understanding of the FFM algorithm, including its advantages and disadvantages, how it works, and how it differs from other recommendation algorithms.