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Paolo Cremonesi
In this course, you will see how to use advanced machine-learning techniques to build more sophisticated recommender systems. Machine Learning is able to provide recommendations and make better predictions, by taking advantage of historical opinions from users and building up the model automatically, without the need for you to think about all the details of the model.
Read more
In this course, you will see how to use advanced machine-learning techniques to build more sophisticated recommender systems. Machine Learning is able to provide recommendations and make better predictions, by taking advantage of historical opinions from users and building up the model automatically, without the need for you to think about all the details of the model.
Read more
In this course, you will see how to use advanced machine-learning techniques to build more sophisticated recommender systems. Machine Learning is able to provide recommendations and make better predictions, by taking advantage of historical opinions from users and building up the model automatically, without the need for you to think about all the details of the model.
At the end of the Advanced Recommender Systems, you will know how to manage hybrid information and how to combine different filtering techniques, taking the best from each approach. More, you will know how to use factorisation machines and represent the input data accordingly and be able to design more sophisticated recommender systems, which can solve the cross-domain recommendation problem.
The course leverages two important EIT Digital Overarching Learning Outcomes (OLOs), related to your creativity and innovation skills. In trying to design a new recommender system you need to think beyond boundaries and try to figure out how you can improve the quality of the outcomes. You should also be able to use knowledge, ideas and technology to create new or significantly improved recommendation tools to support choice-making processes and solve real-life problems in complex and innovative scenarios.
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What's inside
Syllabus
ADVANCED COLLABORATIVE FILTERING
In this first module, we will see how to apply machine learning to collaborative filtering techniques. We will learn how to write an item-based collaborative algorithm which is able to automatically learn the best similarities between items, in order to provide improved recommendations that better match the user opinions predicted by the model with the true user opinions. We will also understand how to train collaborative filtering algorithms that minimize this gap. We will finally define a new error metric based on ranking comparisons, useful to design learning-to-rank algorithms.
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Syllabus
ADVANCED COLLABORATIVE FILTERING
In this first module, we will see how to apply machine learning to collaborative filtering techniques. We will learn how to write an item-based collaborative algorithm which is able to automatically learn the best similarities between items, in order to provide improved recommendations that better match the user opinions predicted by the model with the true user opinions. We will also understand how to train collaborative filtering algorithms that minimize this gap. We will finally define a new error metric based on ranking comparisons, useful to design learning-to-rank algorithms.
Read more
Traffic lights
Traffic lights
Read about what's good
what should give you pause and
possible dealbreakers
Develops advanced recommender systems, which are core skills for tech professionals
Examines factorization machines and cross-domain recommendations, which are highly relevant to industry
Taught by Paolo Cremonesi, who are recognized for their work in recommender systems
This course requires students to come in with extensive background knowledge first
Leverages EIT Digital Overarching Learning Outcomes (OLOs) related to creativity and innovation skills
Provides a hands-on RecSys Challenge to practice and improve skills
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Reviews summary
Solid theory for advanced recommender systems
According to students, this course provides a solid theoretical foundation for advanced recommender systems. Learners found the lectures to be very well-produced and presented , with the instructor doing a great job of explaining complex concepts . However, a key aspect highlighted is the limited hands-on application . Students noted that they had to seek additional resources and documentation to effectively apply the taught methods in practice. This course is deemed ideal for those keen on the theory of recommender systems and applying it to their work, but may be challenging for absolute beginners , suggesting prior basic familiarity.
Best suited for learners already familiar with recommender system basics.
"If you are a beginner, this might be a little difficult, but if you are already familiar with the basics, then you should be fine."
"I found that having a foundational understanding of recommender systems was very helpful before starting."
"This course is ideal if you're looking to deepen existing knowledge rather than starting from scratch."
Lectures are well-produced and concepts are clearly explained.
"I found the lectures to be very well-produced and presented."
"The instructor did a great job of explaining concepts."
"The clarity of the explanations in the lectures was truly impressive."
Provides a deep understanding of advanced recommender systems theory.
"This course is a really great course in that it provides a solid foundation of the theory behind advanced recommendation systems."
"I was able to gain a strong grasp of the theoretical underpinnings of these advanced techniques."
"I found the course excellent for building my understanding of the underlying principles."
Lacks sufficient practical exercises for real-world application.
"it provides little in the way of hands-on applications."
"I found myself having to learn a lot more from other sources and also had to read a lot more documentation outside of the course to be able to properly apply the methods taught in the course."
"I wish there were more opportunities for practical coding and projects within the course itself."
Activities
Be better prepared
before
your course. Deepen your understanding
during
and
after
it. Supplement your coursework and achieve mastery of the topics covered
in Advanced Recommender Systems with these
activities:
Organize Course Notes
Show steps
Organize and review course notes, assignments, and materials to consolidate your understanding of advanced recommender systems concepts.
Show steps
-
Review lecture notes and identify key concepts.
-
Summarize key takeaways from each module.
-
Create a comprehensive study guide for future reference.
Review Machine Learning
Show steps
Refresh your knowledge of machine learning concepts and techniques to strengthen your foundation for this course.
Browse courses on
Machine Learning
Show steps
-
Review linear regression, logistic regression, and decision trees.
-
Practice implementing these algorithms in a programming language.
Review SVD Techniques
Show steps
Review the concepts of Singular Value Decomposition to strengthen the understanding of matrix factorization approaches.
Browse courses on
Singular Value Decomposition
Show steps
-
Go over the basics of linear algebra
-
Define Singular Value Decomposition
-
Understand the relationship between SVD and matrix factorization
11 other activities
Expand to see all activities and additional details
Show all 14 activities
Review SVD techniques
Show steps
Review Singular Value Decomposition (SVD) techniques to strengthen your understanding of dimensionality reduction and matrix factorization approaches used in collaborative filtering.
Show steps
-
Revisit the concept of SVD and its mathematical formulation.
-
Practice applying SVD to real-world datasets using programming tools.
-
Explore different variations of SVD, such as truncated SVD and randomized SVD.
Explore Advanced Recommender Systems Libraries
Show steps
Explore open-source Python libraries for advanced recommender systems, such as Surprise, LightFM, or Recommenders, to enhance your practical skills in implementing these techniques.
Browse courses on
Recommender Systems
Show steps
-
Install and familiarize yourself with a chosen library.
-
Follow tutorials and documentation to implement basic recommendation methods.
-
Experiment with different parameters and configurations to optimize results.
Matrix Factorization Algorithm Exercises
Show steps
Improve understanding of matrix factorization algorithms through practice exercises.
Show steps
-
Solve matrix factorization problems manually
-
Implement matrix factorization algorithms in a programming language
-
Evaluate the performance of matrix factorization algorithms on different datasets
Follow Tutorials on Hybrid Recommender Systems
Show steps
Follow tutorials to gain hands-on experience in building hybrid recommender systems.
Show steps
-
Identify different types of hybrid recommender systems
-
Follow tutorials to implement hybrid recommender systems
-
Experiment with different hybridization techniques
-
Evaluate the performance of hybrid recommender systems
Design a Hybrid Recommender System
Show steps
Design and implement a hybrid recommender system that combines multiple techniques, such as collaborative filtering and content-based filtering, to improve recommendation accuracy and diversity.
Browse courses on
Hybrid Recommender Systems
Show steps
-
Identify the strengths and weaknesses of different recommendation techniques.
-
Select and integrate appropriate techniques to create a hybrid system.
-
Evaluate the performance of the hybrid system using relevant metrics.
Discuss Best Practices for Recommender Systems
Show steps
Engage in discussions with peers to exchange ideas, share experiences, and collectively explore best practices in the field of advanced recommender systems.
Browse courses on
Recommender Systems
Show steps
-
Identify a specific topic or challenge related to recommender systems.
-
Facilitate a discussion with peers, encouraging diverse perspectives.
-
Summarize key insights and takeaways from the discussion.
Solve Factorization Machine Problems
Show steps
Practice solving problems involving Factorization Machines (FMs) to strengthen your understanding of side information incorporation and model optimization.
Show steps
-
Implement a basic FM algorithm using a programming library.
-
Experiment with different feature representations and regularization parameters.
-
Analyze the performance of FM-based models on real-world datasets.
Collaborative Filtering Item-Based Recommender System
Show steps
Create a collaborative filtering item-based recommender system to apply the learning from the course.
Show steps
-
Gather user-item interaction data
-
Compute item-item similarity using cosine similarity
-
Predict user preferences for unrated items
-
Evaluate the recommender system using metrics like precision and recall
Literature Review on Cross-Domain Recommendation
Show steps
Conduct a literature review on cross-domain recommendation techniques to expand your knowledge and identify emerging trends and challenges in this area.
Browse courses on
Recommender Systems
Show steps
-
Search and gather relevant research papers from reputable sources.
-
Summarize and synthesize the key findings and approaches presented in the literature.
-
Discuss the limitations and potential of current cross-domain recommendation methods.
Develop a Recommender System for a Specific Domain
Show steps
Apply your knowledge by developing a recommender system for a specific domain, such as product recommendations, movie recommendations, or news article recommendations.
Browse courses on
Recommender Systems
Show steps
-
Define the problem statement and identify the target audience.
-
Gather and prepare the necessary data.
-
Select and implement appropriate recommendation techniques.
-
Evaluate the performance of the recommender system and iterate to improve results.
Tutorial on Factorization Machines
Show steps
Create a tutorial to explain the concepts and applications of Factorization Machines.
Show steps
-
Explain the intuition behind Factorization Machines
-
Discuss the mathematical formulation of Factorization Machines
-
Provide examples of using Factorization Machines for real-world problems
-
Publish the tutorial online
Organize Course Notes
Show steps
Organize and review course notes, assignments, and materials to consolidate your understanding of advanced recommender systems concepts.
Show steps
Show steps
Show steps
- Review lecture notes and identify key concepts.
- Summarize key takeaways from each module.
- Create a comprehensive study guide for future reference.
Review Machine Learning
Show steps
Refresh your knowledge of machine learning concepts and techniques to strengthen your foundation for this course.
Browse courses on
Machine Learning
Show steps
Show steps
Show steps
- Review linear regression, logistic regression, and decision trees.
- Practice implementing these algorithms in a programming language.
Review SVD Techniques
Show steps
Review the concepts of Singular Value Decomposition to strengthen the understanding of matrix factorization approaches.
Browse courses on
Singular Value Decomposition
Show steps
Show steps
Show steps
- Go over the basics of linear algebra
- Define Singular Value Decomposition
- Understand the relationship between SVD and matrix factorization
11 other activities
Expand to see all activities and additional details
Show all 14 activities
Review SVD techniques
Show steps
Review Singular Value Decomposition (SVD) techniques to strengthen your understanding of dimensionality reduction and matrix factorization approaches used in collaborative filtering.
Show steps
Show steps
Show steps
- Revisit the concept of SVD and its mathematical formulation.
- Practice applying SVD to real-world datasets using programming tools.
- Explore different variations of SVD, such as truncated SVD and randomized SVD.
Explore Advanced Recommender Systems Libraries
Show steps
Explore open-source Python libraries for advanced recommender systems, such as Surprise, LightFM, or Recommenders, to enhance your practical skills in implementing these techniques.
Browse courses on
Recommender Systems
Show steps
Show steps
Show steps
- Install and familiarize yourself with a chosen library.
- Follow tutorials and documentation to implement basic recommendation methods.
- Experiment with different parameters and configurations to optimize results.
Matrix Factorization Algorithm Exercises
Show steps
Improve understanding of matrix factorization algorithms through practice exercises.
Show steps
Show steps
Show steps
- Solve matrix factorization problems manually
- Implement matrix factorization algorithms in a programming language
- Evaluate the performance of matrix factorization algorithms on different datasets
Follow Tutorials on Hybrid Recommender Systems
Show steps
Follow tutorials to gain hands-on experience in building hybrid recommender systems.
Show steps
Show steps
Show steps
- Identify different types of hybrid recommender systems
- Follow tutorials to implement hybrid recommender systems
- Experiment with different hybridization techniques
- Evaluate the performance of hybrid recommender systems
Design a Hybrid Recommender System
Show steps
Design and implement a hybrid recommender system that combines multiple techniques, such as collaborative filtering and content-based filtering, to improve recommendation accuracy and diversity.
Browse courses on
Hybrid Recommender Systems
Show steps
Show steps
Show steps
- Identify the strengths and weaknesses of different recommendation techniques.
- Select and integrate appropriate techniques to create a hybrid system.
- Evaluate the performance of the hybrid system using relevant metrics.
Discuss Best Practices for Recommender Systems
Show steps
Engage in discussions with peers to exchange ideas, share experiences, and collectively explore best practices in the field of advanced recommender systems.
Browse courses on
Recommender Systems
Show steps
Show steps
Show steps
- Identify a specific topic or challenge related to recommender systems.
- Facilitate a discussion with peers, encouraging diverse perspectives.
- Summarize key insights and takeaways from the discussion.
Solve Factorization Machine Problems
Show steps
Practice solving problems involving Factorization Machines (FMs) to strengthen your understanding of side information incorporation and model optimization.
Show steps
Show steps
Show steps
- Implement a basic FM algorithm using a programming library.
- Experiment with different feature representations and regularization parameters.
- Analyze the performance of FM-based models on real-world datasets.
Collaborative Filtering Item-Based Recommender System
Show steps
Create a collaborative filtering item-based recommender system to apply the learning from the course.
Show steps
Show steps
Show steps
- Gather user-item interaction data
- Compute item-item similarity using cosine similarity
- Predict user preferences for unrated items
- Evaluate the recommender system using metrics like precision and recall
Literature Review on Cross-Domain Recommendation
Show steps
Conduct a literature review on cross-domain recommendation techniques to expand your knowledge and identify emerging trends and challenges in this area.
Browse courses on
Recommender Systems
Show steps
Show steps
Show steps
- Search and gather relevant research papers from reputable sources.
- Summarize and synthesize the key findings and approaches presented in the literature.
- Discuss the limitations and potential of current cross-domain recommendation methods.
Develop a Recommender System for a Specific Domain
Show steps
Apply your knowledge by developing a recommender system for a specific domain, such as product recommendations, movie recommendations, or news article recommendations.
Browse courses on
Recommender Systems
Show steps
Show steps
Show steps
- Define the problem statement and identify the target audience.
- Gather and prepare the necessary data.
- Select and implement appropriate recommendation techniques.
- Evaluate the performance of the recommender system and iterate to improve results.
Tutorial on Factorization Machines
Show steps
Create a tutorial to explain the concepts and applications of Factorization Machines.
Show steps
Show steps
Show steps
- Explain the intuition behind Factorization Machines
- Discuss the mathematical formulation of Factorization Machines
- Provide examples of using Factorization Machines for real-world problems
- Publish the tutorial online
Career center
Learners who complete Advanced Recommender Systems will develop knowledge and skills
that may be useful to these careers:
Data Scientist
Data Scientist
Data Scientists are responsible for collecting, analyzing, and interpreting大量的 data. They use this data to help businesses make better decisions. Advanced Recommender Systems can help Data Scientists develop new and innovative ways to collect and analyze data. This course can also help Data Scientists learn how to use machine learning to build more sophisticated recommender systems. This can help them make better predictions and recommendations, which can lead to improved business outcomes.
Market Researcher
Market Researcher
Market Researchers study market trends and customer behavior. They use this information to help businesses develop new products and services. Advanced Recommender Systems can help Market Researchers understand customer preferences and make better recommendations. This course can also help Market Researchers learn how to use machine learning to build more sophisticated recommender systems. This can help them make better predictions and recommendations, which can lead to improved business outcomes.
Business Analyst
Business Analyst
Business Analysts help businesses solve problems and improve efficiency. They use data and analysis to identify opportunities and develop solutions. Advanced Recommender Systems can help Business Analysts improve their data analysis skills. This course can also help Business Analysts learn how to use machine learning to build more sophisticated recommender systems.
Product Manager
Product Manager
Product Managers are responsible for developing and managing products. They work with engineers, designers, and marketers to bring products to market. Advanced Recommender Systems can help Product Managers understand customer needs and develop better products. This course can also help Product Managers learn how to use machine learning to build more sophisticated recommender systems.
Software Engineer
Software Engineer
Software Engineers design, develop, and maintain software applications. They work with computers and other hardware to create software solutions. Advanced Recommender Systems can help Software Engineers build more sophisticated recommender systems. This course can also help Software Engineers learn how to use machine learning to improve the performance of their software applications.
Data Engineer
Data Engineer
Data Engineers design and build the infrastructure that stores and processes data. They work with data scientists and other engineers to ensure that data is available and accessible. Advanced Recommender Systems can help Data Engineers build more sophisticated recommender systems. This course can also help Data Engineers learn how to use machine learning to improve the performance of their data infrastructure.
Machine Learning Engineer
Machine Learning Engineer
Machine Learning Engineers design and build machine learning models. They work with data scientists and other engineers to develop and deploy machine learning solutions. Advanced Recommender Systems can help Machine Learning Engineers build more sophisticated recommender systems. This course can also help Machine Learning Engineers learn how to use machine learning to improve the performance of their machine learning models.
Statistician
Statistician
Statisticians collect, analyze, and interpret data. They work with businesses, governments, and other organizations to help them make better decisions. Advanced Recommender Systems can help Statisticians develop new and innovative ways to collect and analyze data. This course can also help Statisticians learn how to use machine learning to build more sophisticated recommender systems.
User Experience Researcher
User Experience Researcher
User Experience Researchers study how users interact with products and services. They use this information to improve the user experience. Advanced Recommender Systems can help User Experience Researchers understand user preferences and make better recommendations. This course can also help User Experience Researchers learn how to use machine learning to build more sophisticated recommender systems.
Quantitative Analyst
Quantitative Analyst
Quantitative Analysts use mathematical and statistical models to analyze data. They work with businesses and governments to help them make better decisions. Advanced Recommender Systems can help Quantitative Analysts develop new and innovative ways to collect and analyze data. This course can also help Quantitative Analysts learn how to use machine learning to build more sophisticated recommender systems.
Actuary
Actuary
Actuaries use mathematical and statistical models to assess risk. They work with insurance companies and other financial institutions to help them make better decisions. Advanced Recommender Systems can help Actuaries develop new and innovative ways to collect and analyze data. This course can also help Actuaries learn how to use machine learning to build more sophisticated recommender systems.
Operations Research Analyst
Operations Research Analyst
Operations Research Analysts use mathematical and statistical models to improve the efficiency of operations. They work with businesses and governments to help them make better decisions. Advanced Recommender Systems can help Operations Research Analysts develop new and innovative ways to collect and analyze data. This course can also help Operations Research Analysts learn how to use machine learning to build more sophisticated recommender systems.
Data Analyst
Data Analyst
Data Analysts collect, analyze, and interpret data. They work with businesses and governments to help them make better decisions. Advanced Recommender Systems can help Data Analysts develop new and innovative ways to collect and analyze data. This course can also help Data Analysts learn how to use machine learning to build more sophisticated recommender systems.
For more career information including salaries, visit:
OpenCourser.com/course/tuwli6/advanced
Reading list
We've selected 14 books
that we think will supplement your
learning. Use these to
develop background knowledge, enrich your coursework, and gain a
deeper understanding of the topics covered in
Advanced Recommender Systems.
Is commonly used as a textbook at academic institutions
Practical guide to building recommender systems. It valuable resource for practitioners who want to learn how to build and deploy recommender systems.
Comprehensive textbook on recommender systems. It valuable resource for students, researchers, and practitioners who want to learn more about this important topic.
This paper provides a detailed overview of factorization machines for recommender systems. It valuable resource for researchers and practitioners who want to learn more about this important topic.
Save
Offers an in-depth treatment of SVD and other dimensionality reduction methods, including their theoretical foundations, numerical implementation, and applications in various domains. Excellent reference for understanding the mathematical foundations of matrix factorization techniques.
Provides a comprehensive overview of probabilistic graphical models, including their theoretical foundations, numerical implementation, and applications in various domains. Offers a thorough treatment of advanced topics, such as deep learning.
Save
Provides a comprehensive overview of machine learning, including its theoretical foundations, numerical implementation, and applications in various domains. Offers a thorough treatment of advanced topics, such as deep learning.
Save
Provides a comprehensive overview of deep learning, including its theoretical foundations, numerical implementation, and applications in various domains. Offers a thorough treatment of advanced topics, such as deep reinforcement learning.
Provides a comprehensive overview of reinforcement learning, including its theoretical foundations, numerical implementation, and applications in various domains. Offers a thorough treatment of advanced topics, such as deep reinforcement learning.
Provides a comprehensive overview of information retrieval, including its theoretical foundations, numerical implementation, and applications in various domains. Offers a thorough treatment of advanced topics, such as deep learning.
Provides a comprehensive overview of natural language processing with Python, including its theoretical foundations, numerical implementation, and applications in various domains. Offers a thorough treatment of advanced topics, such as deep learning.
Save
Provides a comprehensive overview of computer vision, including its theoretical foundations, numerical implementation, and applications in various domains. Offers a thorough treatment of advanced topics, such as deep learning.
Provides a comprehensive overview of speech and language processing, including its theoretical foundations, numerical implementation, and applications in various domains. Offers a thorough treatment of advanced topics, such as deep learning.
Save
Provides a comprehensive overview of data mining, including its theoretical foundations, numerical implementation, and applications in various domains. Offers a thorough treatment of advanced topics, such as deep learning.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/tuwli6/advanced
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Effort
4 weeks of study (an average of 15-20 hours per week)
Level
Intermediate
Via
Coursera
Institutions
Politecnico di Milano
EIT Digital
Instructor
Paolo Cremonesi
Language
English
Transcripts
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