Advanced Recommender Systems
Master advanced ML techniques for recommender systems. Learn collaborative filtering, SVD, hybrid systems, and factorization machines.
Master advanced ML techniques for recommender systems. Learn collaborative filtering, SVD, hybrid systems, and factorization machines.
Dive into advanced machine learning techniques for building sophisticated recommender systems. This course covers collaborative filtering, singular value decomposition, hybrid systems, and factorization machines. Learn to integrate side information, combine different filtering techniques, and solve cross-domain recommendation problems. Gain hands-on experience with a practical RecSys Challenge, applying your skills to real-world e-commerce data. Ideal for those with basic knowledge of recommender systems and linear algebra, this course will elevate your ability to design and implement cutting-edge recommendation algorithms.
3.8
(23 ratings)
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What you'll learn
Use machine learning techniques to build advanced recommender systems
Design and implement collaborative filtering algorithms with improved accuracy
Apply singular value decomposition (SVD) techniques for dimensionality reduction
Create hybrid recommender systems combining multiple filtering approaches
Integrate content and contextual information into recommendation algorithms
Implement factorization machines for sophisticated predictions
Skills you'll gain
This course includes:
1 Hours PreRecorded video
4 quizzes,1 programming assignment
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FullTime access
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There are 5 modules in this course
This course delves into advanced techniques for building sophisticated recommender systems using machine learning. Students will explore advanced collaborative filtering, singular value decomposition (SVD) techniques, hybrid and context-aware recommender systems, and factorization machines. The curriculum covers how to combine different filtering techniques, integrate side information (content or context), and solve cross-domain recommendation problems. Practical skills are developed through hands-on exercises and a RecSys Challenge using real e-commerce data. By the end of the course, participants will be able to design and implement cutting-edge recommendation algorithms that can improve prediction accuracy and user experience in various domains.
ADVANCED COLLABORATIVE FILTERING
Module 1 · 2 Hours to complete
SINGULAR VALUE DECOMPOSITION TECHNIQUES - SVD
Module 2 · 2 Hours to complete
HYBRID AND CONTEXT AWARE RECOMMENDER SYSTEMS
Module 3 · 3 Hours to complete
FACTORIZATION MACHINES
Module 4 · 2 Hours to complete
Recsys Challenge (Honors)
Module 5 · 4 Hours to complete
Fee Structure
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Instructor
Associate Professor at Politecnico di Milano and EIT Digital Coordinator
Dr. Paolo Cremonesi is an Associate Professor in the Computer Science Department at Politecnico di Milano, where he also serves as the local coordinator for the EIT Digital double degree program in Data Science. His research focuses on key areas such as recommender systems, machine learning, predictive models, and high-performance computing. Dr. Cremonesi has made significant contributions to the field, including participation in projects like the development of the Hierarchical Recurrent Neural Network (HRNN) for Amazon Personalize, a machine learning service that provides recommendation models. He is actively involved in academic collaborations and has contributed to various research initiatives, including winning first place in the academic part of the RecSys Challenge 2021. His work is widely recognized in the academic community, and he continues to advance research in dynamic recommender systems and user models.
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3.8 course rating
23 ratings
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