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Joseph A Konstan
and
Michael D. Ekstrand
In this course, you will learn the fundamental techniques for making personalized recommendations through nearest-neighbor techniques. First you will learn user-user collaborative filtering, an algorithm that identifies other people with similar tastes to a target user and combines their ratings to make recommendations for that user. You will explore and implement variations of the user-user algorithm, and will explore the benefits and drawbacks of the general approach. Then you will learn the widely-practiced item-item collaborative filtering algorithm, which identifies global product associations from user ratings, but uses these product associations to provide personalized recommendations based on a user's own product ratings.
Register for this course and see more details by visiting:
OpenCourser.com/course/gqlmpn/nearest
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What's inside
Syllabus
Preface
Note that this course is structured into two-week chunks. The first chunk focuses on User-User Collaborative Filtering; the second chunk on Item-Item Collaborative Filtering. Each chunk has most of the lectures in the first week, and assignments/quizzes and advanced topics in the second week. We encourage learners to treat each two-week chunk as one unit, starting the assignments as soon as they feel they have learned enough to get going.
Read more
Syllabus
Preface
Note that this course is structured into two-week chunks. The first chunk focuses on User-User Collaborative Filtering; the second chunk on Item-Item Collaborative Filtering. Each chunk has most of the lectures in the first week, and assignments/quizzes and advanced topics in the second week. We encourage learners to treat each two-week chunk as one unit, starting the assignments as soon as they feel they have learned enough to get going.
Read more
User-User Collaborative Filtering Recommenders Part 1
User-User Collaborative Filtering Recommenders Part 2
Item-Item Collaborative Filtering Recommenders Part 1
Item-Item Collaborative Filtering Recommenders Part 2
Advanced Collaborative Filtering Topics
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Explores nearest-neighbor techniques, which are fundamental to designing personalized recommenders
Taught by experienced instructors from the University of Minnesota, who are recognized for their work in collaborative filtering
Focuses on the widely-used item-item collaborative filtering algorithm, which helps learners build personalized recommendation systems
Provides step-by-step implementation guides for various collaborative filtering algorithms, strengthening learners' practical skills
Prerequisites include a basic understanding of machine learning and data mining, making it suitable for intermediate learners
Requires proficiency in Python, as learners will work with real-world datasets for practical exercises
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Reviews summary
Low-rated course
According to students, Nearest Neighbor Collaborative Filtering is poorly received and may need improvement. Students have reported concerns about the course content, citing unclear examples. There have also been complaints about lack of response from the instructor.
Instructor does not respond to feedback.
"Not clear examples in my opinion, and there was same complain made from several user and I never saw a reply and nothing was changed"
Course could use clearer examples.
"Not clear examples in my opinion, and there was same complain made from several user and I never saw a reply and nothing was changed"
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 Nearest Neighbor Collaborative Filtering with these
activities:
Review Matrix Operations
Show steps
Sharpen your understanding of matrix operations, a fundamental concept used in recommender systems.
Browse courses on
Matrix Operations
Show steps
-
Review matrix basics and properties.
-
Practice matrix transformations.
-
Solve matrix equations.
Design a User-User Collaborative Filtering Algorithm
Show steps
Create a logical flow of how a user-user collaborative filtering algorithm works.
Show steps
-
Understand the principles of user-user collaborative filtering.
-
Design the algorithm's architecture.
-
Implement the algorithm.
-
Test and evaluate the algorithm.
Follow Tutorials on Item-Item Collaborative Filtering
Show steps
Learn the practical aspects of item-item collaborative filtering through guided tutorials.
Show steps
-
Identify suitable tutorials.
-
Follow and understand the tutorials.
-
Practice implementing the techniques.
One other activity
Expand to see all activities and additional details
Show all four activities
Participate in Discussion Forums
Show steps
Engage with peers to clarify concepts, exchange ideas, and deepen your understanding.
Show steps
-
Join discussion forums.
-
Actively participate in discussions.
-
Ask and answer questions.
Review Matrix Operations
Show steps
Sharpen your understanding of matrix operations, a fundamental concept used in recommender systems.
Browse courses on
Matrix Operations
Show steps
Show steps
Show steps
- Review matrix basics and properties.
- Practice matrix transformations.
- Solve matrix equations.
Design a User-User Collaborative Filtering Algorithm
Show steps
Create a logical flow of how a user-user collaborative filtering algorithm works.
Show steps
Show steps
Show steps
- Understand the principles of user-user collaborative filtering.
- Design the algorithm's architecture.
- Implement the algorithm.
- Test and evaluate the algorithm.
Follow Tutorials on Item-Item Collaborative Filtering
Show steps
Learn the practical aspects of item-item collaborative filtering through guided tutorials.
Show steps
Show steps
Show steps
- Identify suitable tutorials.
- Follow and understand the tutorials.
- Practice implementing the techniques.
One other activity
Expand to see all activities and additional details
Show all four activities
Participate in Discussion Forums
Show steps
Engage with peers to clarify concepts, exchange ideas, and deepen your understanding.
Show steps
Show steps
Show steps
- Join discussion forums.
- Actively participate in discussions.
- Ask and answer questions.
Career center
Learners who complete Nearest Neighbor Collaborative Filtering will develop knowledge and skills
that may be useful to these careers:
Data Scientist
Data Scientist
Data Scientists create and develop algorithms that find patterns and trends in data. As a Data Scientist, you will need to be able to analyze large datasets and identify meaningful patterns. This course in Nearest Neighbor Collaborative Filtering will provide you with a foundation in the techniques used to make personalized recommendations based on user data. With this knowledge, you will be better prepared to develop the algorithms that are used by businesses to tailor their products and services to individual customers.
Market Researcher
Market Researcher
Market Researchers gather and analyze data about consumer behavior. They use this data to help businesses understand their target market and develop marketing strategies. This course in Nearest Neighbor Collaborative Filtering will provide you with a foundation in the techniques used to make personalized recommendations based on user data. With this knowledge, you will be better prepared to gather and analyze data about consumer behavior and make recommendations to businesses about how to reach their target market.
Business Analyst
Business Analyst
Business Analysts use data to identify and solve business problems. They work with businesses to understand their needs and develop solutions that will improve their performance. This course in Nearest Neighbor Collaborative Filtering will provide you with a foundation in the techniques used to analyze data and identify patterns. With this knowledge, you will be better prepared to solve business problems and improve the performance of businesses.
Product Manager
Product Manager
Product Managers oversee the development and launch of new products. They work with marketing, engineering, and design teams to bring new products to market. This course in Nearest Neighbor Collaborative Filtering will provide you with a foundation in the techniques used to understand consumer behavior and make recommendations. With this knowledge, you will be better prepared to develop products that meet the needs of customers.
Marketing Manager
Marketing Manager
Marketing Managers develop and implement marketing campaigns. They work with marketing teams to develop and execute marketing strategies. This course in Nearest Neighbor Collaborative Filtering will provide you with a foundation in the techniques used to understand consumer behavior and make recommendations. With this knowledge, you will be better prepared to develop and implement marketing campaigns that are effective at reaching target audiences.
Consultant
Consultant
Consultants provide businesses with advice on how to improve their performance. They work with businesses to identify problems and develop solutions. This course in Nearest Neighbor Collaborative Filtering will provide you with a foundation in the techniques used to analyze data and identify patterns. With this knowledge, you will be better prepared to provide businesses with advice on how to improve their performance.
Data Analyst
Data Analyst
Data Analysts collect, clean, and analyze data. They work with businesses to understand their data and make recommendations for how to improve their operations. This course in Nearest Neighbor Collaborative Filtering will provide you with a foundation in the techniques used to analyze data and identify patterns. With this knowledge, you will be better prepared to collect, clean, and analyze data and make recommendations to businesses for how to improve their operations.
Software Engineer
Software Engineer
Software Engineers design, develop, and maintain software applications. They work with businesses to understand their needs and develop software that meets those needs. This course in Nearest Neighbor Collaborative Filtering may be useful for Software Engineers who are interested in developing recommendation systems.
Web Developer
Web Developer
Web Developers design and develop websites. They work with businesses to understand their needs and develop websites that meet those needs. This course in Nearest Neighbor Collaborative Filtering may be useful for Web Developers who are interested in developing recommendation systems for websites.
Database Administrator
Database Administrator
Database Administrators manage and maintain databases. They work with businesses to ensure that their databases are running smoothly and that data is secure. This course in Nearest Neighbor Collaborative Filtering may be useful for Database Administrators who are interested in using recommendation systems to improve the performance of their databases.
Systems Analyst
Systems Analyst
Systems Analysts design and implement computer systems. They work with businesses to understand their needs and develop systems that meet those needs. This course in Nearest Neighbor Collaborative Filtering may be useful for Systems Analysts who are interested in using recommendation systems to improve the performance of their systems.
Network Administrator
Network Administrator
Network Administrators manage and maintain computer networks. They work with businesses to ensure that their networks are running smoothly and that data is secure. This course in Nearest Neighbor Collaborative Filtering may be useful for Network Administrators who are interested in using recommendation systems to improve the performance of their networks.
Computer Programmer
Computer Programmer
Computer Programmers write and maintain computer programs. They work with businesses to understand their needs and develop programs that meet those needs. This course in Nearest Neighbor Collaborative Filtering may be useful for Computer Programmers who are interested in using recommendation systems in their programs.
Technical Writer
Technical Writer
Technical Writers create and maintain documentation for computer software and hardware. They work with businesses to explain how their products work and how to use them. This course in Nearest Neighbor Collaborative Filtering may be useful for Technical Writers who are interested in using recommendation systems to improve the quality of their documentation.
Quality Assurance Analyst
Quality Assurance Analyst
Quality Assurance Analysts test and evaluate computer software and hardware. They work with businesses to ensure that their products meet quality standards. This course in Nearest Neighbor Collaborative Filtering may be useful for Quality Assurance Analysts who are interested in using recommendation systems to improve the quality of their testing.
For more career information including salaries, visit:
OpenCourser.com/course/gqlmpn/nearest
Reading list
We've selected six 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
Nearest Neighbor Collaborative Filtering.
Provides a comprehensive overview of deep learning techniques for building recommender systems. It covers a wide range of topics, including deep neural networks, convolutional neural networks, and recurrent neural networks.
Provides a practical guide to building recommender systems. It covers a wide range of topics, including data preparation, model selection, and evaluation.
Provides a comprehensive overview of machine learning techniques for building recommender systems. It covers a wide range of topics, from basic concepts to advanced algorithms.
While this book focuses on information retrieval, it provides a solid foundation for understanding various concepts and techniques that are also applicable to recommender systems, such as text mining, natural language processing, and machine learning.
Save
Provides a comprehensive overview of data mining techniques, including clustering, classification, and association rule mining. These techniques are essential for building effective recommender systems.
Save
As machine learning forms the foundation of many recommender systems, a book covering its core concepts, algorithms, and techniques would greatly enhance the understanding of such systems.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/gqlmpn/nearest
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Institution
University of Minnesota
Instructors
Joseph A Konstan
Michael D. Ekstrand
Language
English
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آذربایجان دیلی
This course belongs to a
collection
called
Recommender Systems. It's comprised of five courses:
- Introduction to Recommender Systems: Non-Personalized and Content-Based
- Nearest Neighbor Collaborative Filtering (viewing)
- Recommender Systems Capstone
- Matrix Factorization and Advanced Techniques
- Recommender Systems: Evaluation and Metrics
Good to know
Explores nearest-neighbor techniques, which are fundamental to designing personalized recommenders
Taught by experienced instructors from the University of Minnesota, who are recognized for their work in collaborative filtering
Focuses on the widely-used item-item collaborative filtering algorithm, which helps learners build personalized recommendation systems
Provides step-by-step implementation guides for various collaborative filtering algorithms, strengthening learners' practical skills
Prerequisites include a basic understanding of machine learning and data mining, making it suitable for intermediate learners
Requires proficiency in Python, as learners will work with real-world datasets for practical exercises
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