Sinduja Subramaniam
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Staff Data Scientist
Walmart |
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Sinduja is a Staff Data Scientist at Walmart Labs in California location, and has close to 6 years of experience in tackling personalization challenges and problems. She has a masters degree in computer science - specializing in big data and machine learning - from University of Illinois at Urbana Champaign. As part of her role at Walmart Labs, besides being an independent contributor, she also manages a team of data scientists. She leads data and relevance initiatives around customer's repurchase journey and home page personalization and holds multiple patents relevant to these initiatives.
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Workshop: Evolution of Applied Recommender Systems
Prerequisite: Basics of Linear Algebra and Linear Algebra for Regression; Concept of Features, Response & Parameters; Basics of Clustering and Neural Networks.
Machine learning driven Recommender Systems are undeniably one of the most crucial applications in modern technology. In this age of information, we are all in business with matching people to products, services, interests, information – you name it. Today, we depend on search engines and websites to show us what we like even before we know it! But the state-of-the-art Recommender Systems we know today are a result of consistent research taking shape for over three decades. In this workshop, we take you through the whirlwind journey of the recommender system from GroupLens in the 1990s, Content Based Filtering, Matrix Factorization and Hybrid Recommender Systems in the late 2000s all the way to DeepLearning based recommenders of today.
Machine learning driven Recommender Systems are undeniably one of the most crucial applications in modern technology. In this age of information, we are all in business with matching people to products, services, interests, information – you name it. Today, we depend on search engines and websites to show us what we like even before we know it! But the state-of-the-art Recommender Systems we know today are a result of consistent research taking shape for over three decades. In this workshop, we take you through the whirlwind journey of the recommender system from GroupLens in the 1990s, Content Based Filtering, Matrix Factorization and Hybrid Recommender Systems in the late 2000s all the way to DeepLearning based recommenders of today.
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