The Ultimate Matchmaker: Building Recommender Systems with TensorFlow
Room: Coral A - Workshop
Time: 11:00 - 11:55
Are you curious about how recommendation engines work? In this workshop, we'll dive deep into the world of TensorFlow Recommender Systems, exploring the fundamental concepts, techniques, and tools needed to build effective recommendation engines. We'll start with an overview of the different types of recommender systems, including collaborative filtering, content-based filtering, and hybrid models. We'll also explore evaluation metrics and learn how to measure the effectiveness of these models. The workshop then shifts to hands-on exercises that allow you to build your own recommendation engine using TensorFlow. You'll learn how to prepare data, train the model, and make recommendations. Through guided examples, you'll gain a practical understanding of the end-to-end process of building a recommendation engine.
Ashmi is a doctoral researcher at the Technical University of Munich, where her research focuses on Recommender Systems and Human-Computer Interaction. She graduated with a master's degree in Computer Science in 2019 from the same university. She is passionate about using technology to automate tedious tasks and is always excited to tackle new technical challenges. Recently, she was honored with the DevelopHER Awards 2022 in the Emerging Talent category by DevelopHER UK. Since 2023, she also holds the title of Google Developer Expert (GDE) in Machine Learning. As a Google Women Techmakers (WTM) Ambassador diversity advocate, she is dedicated to closing the gender gap in STEM through her involvement in various women in STEM networks. When not sitting in front of her computer, she is either traveling or training to become a triathlete. 🏊♀️ 🚴 🏃♀️