Basic Recommender Systems
Learn key approaches in recommender systems. Master collaborative and content-based techniques for effective recommendations.
Learn key approaches in recommender systems. Master collaborative and content-based techniques for effective recommendations.
This course introduces you to leading approaches in recommender systems, covering both collaborative and content-based techniques. You'll learn how these systems work, how to use them, and how to evaluate their performance. The course equips you with tools to measure recommender system quality and improve it through new algorithm design. You'll explore ethical considerations like identity, privacy, and manipulation. By the end, you'll be able to describe recommender system requirements for different domains, distinguish systems by input data and mechanisms, and design tailored systems for new applications.
4.3
(41 ratings)
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What you'll learn
Build a basic recommender system
Choose appropriate recommender system types based on input data and goals
Identify correct evaluation methods for measuring recommender system quality
Understand benefits and limitations of different recommender techniques
Apply collaborative and content-based filtering approaches
Design recommender systems for new application domains
Skills you'll gain
This course includes:
123 Minutes PreRecorded video
4 quizzes
Access on Mobile, Tablet, Desktop
FullTime access
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There are 4 modules in this course
This course provides a comprehensive introduction to recommender systems, covering the leading approaches in both collaborative and content-based techniques. Students will learn how these systems work, how to implement them, and how to evaluate their performance. The curriculum covers the most important algorithms used in recommender systems, teaching students to distinguish between different types based on input data, internal mechanisms, and goals. Practical skills in measuring and improving recommender system quality are emphasized, along with the ability to design systems for new application domains. The course also addresses important ethical considerations in recommender system design, including issues of identity, privacy, and manipulation.
BASIC CONCEPTS
Module 1 · 2 Hours to complete
EVALUATION OF RECOMMENDER SYSTEMS
Module 2 · 2 Hours to complete
CONTENT-BASED FILTERING
Module 3 · 3 Hours to complete
COLLABORATIVE FILTERING
Module 4 · 2 Hours to complete
Fee Structure
Payment options
Financial Aid
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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4.3 course rating
41 ratings
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