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Joseph A Konstan
and
Michael D. Ekstrand
This course, which is designed to serve as the first course in the Recommender Systems specialization, introduces the concept of recommender systems, reviews several examples in detail, and leads you through non-personalized recommendation using summary statistics and product associations, basic stereotype-based or demographic recommendations, and content-based filtering recommendations.
Read more
This course, which is designed to serve as the first course in the Recommender Systems specialization, introduces the concept of recommender systems, reviews several examples in detail, and leads you through non-personalized recommendation using summary statistics and product associations, basic stereotype-based or demographic recommendations, and content-based filtering recommendations.
Read more
This course, which is designed to serve as the first course in the Recommender Systems specialization, introduces the concept of recommender systems, reviews several examples in detail, and leads you through non-personalized recommendation using summary statistics and product associations, basic stereotype-based or demographic recommendations, and content-based filtering recommendations.
After completing this course, you will be able to compute a variety of recommendations from datasets using basic spreadsheet tools, and if you complete the honors track you will also have programmed these recommendations using the open source LensKit recommender toolkit.
In addition to detailed lectures and interactive exercises, this course features interviews with several leaders in research and practice on advanced topics and current directions in recommender systems.
Register for this course and see more details by visiting:
OpenCourser.com/course/95mol5/introduction
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What's inside
Syllabus
Preface
This brief module introduces the topic of recommender systems (including placing the technology in historical context) and provides an overview of the structure and coverage of the course and specialization.
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Syllabus
Preface
This brief module introduces the topic of recommender systems (including placing the technology in historical context) and provides an overview of the structure and coverage of the course and specialization.
Read more
Traffic lights
Traffic lights
Read about what's good
what should give you pause and
possible dealbreakers
Teaches the foundational concepts of recommender systems, a highly relevant toolset in industry and academia
Builds a foundational understanding of recommender systems for beginners
Introduces the concept of recommender systems, which is a core aspect of many modern technologies
Provides hands-on exercises in spreadsheet tools, helping learners apply concepts immediately
Introduces the foundational concepts of recommender systems
Offers three types of profile and prediction exercises in a spreadsheet
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Reviews summary
Solid introduction to recommender systems basics
According to learners, this course provides a solid introduction to the field of recommender systems, covering non-personalized and content-based methods effectively. Many find the explanations clear and easy to follow, making complex topics accessible. The spreadsheet assignments and the introduction to the LensKit toolkit are highlighted as particularly useful for hands-on learning, helping students solidify their understanding of the foundational concepts. While it is acknowledged as an introductory course, some learners note it primarily covers the basics and may lack depth on more advanced techniques.
Assignments aid in applying learned concepts.
"The spreadsheet assignments were very useful for getting hands-on experience with the calculations."
"Working with LensKit was a great way to see how these methods are implemented in practice."
"The exercises truly helped solidify the theoretical concepts presented in the lectures."
"I enjoyed the practical assignments that allowed me to apply the algorithms learned."
Concepts are explained clearly and simply.
"The lectures are very clear and easy to follow, even for complex topics."
"Instructor explains things very well, making it easy to grasp new concepts."
"I appreciated the clear explanations and step-by-step approach taken in the videos."
"The course material was presented in a way that was easy to understand and digest."
Provides a strong basis for understanding RS.
"This course provides a great introduction to the field of recommender systems..."
"A very good and solid base course for learning about recommender systems..."
"It lays a strong foundation for understanding the core concepts before diving into more complex areas."
"I found this course to be an excellent starting point for someone new to recommender systems."
Setting up the LensKit environment had issues.
"Had some difficulty setting up the LensKit environment; instructions could be clearer."
"The technical setup for the honors track using LensKit was a bit challenging."
"encountered minor issues with the recommended software setup for the programming part."
Covers basics but not advanced topics.
"As an introduction, it doesn't go into much depth on advanced techniques, which is expected but noted."
"While it's a good overview, those looking for advanced algorithms might find it too basic."
"This course is purely foundational; you'll need subsequent courses for more complex areas."
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 Introduction to Recommender Systems: Non-Personalized and Content-Based with these
activities:
Explore advanced recommender systems techniques
Show steps
Expand knowledge of recommender systems beyond the course materials
Show steps
-
Identify advanced recommender systems techniques
-
Find online tutorials or courses on these techniques
-
Follow the tutorials and implement the techniques
Show all one activities
Explore advanced recommender systems techniques
Show steps
Expand knowledge of recommender systems beyond the course materials
Show steps
Show steps
Show steps
- Identify advanced recommender systems techniques
- Find online tutorials or courses on these techniques
- Follow the tutorials and implement the techniques
Show all one activities
Career center
Learners who complete Introduction to Recommender Systems: Non-Personalized and Content-Based will develop knowledge and skills
that may be useful to these careers:
Data Analyst
Data Analyst
Data Analysts play a critical role in the success of modern businesses. They are responsible for collecting, cleaning, and analyzing data to extract meaningful insights. This course can help you develop the skills necessary to become a successful Data Analyst by providing you with a solid foundation in recommender systems. Recommender systems are used by companies like Amazon and Netflix to personalize the user experience and increase sales. By understanding how recommender systems work, you will be able to better understand how data can be used to improve decision-making and drive business success.
Product Manager
Product Manager
Product Managers are responsible for the development and launch of new products. They work closely with engineers, designers, and marketers to ensure that products meet the needs of customers. This course can help you develop the skills necessary to become a successful Product Manager by providing you with a solid foundation in recommender systems. Recommender systems are used by companies like Amazon and Netflix to personalize the user experience and increase sales. By understanding how recommender systems work, you will be able to better understand how to develop products that meet the needs of customers.
Marketing Manager
Marketing Manager
Marketing Managers are responsible for developing and executing marketing campaigns. They work closely with sales teams to generate leads and drive sales. This course can help you develop the skills necessary to become a successful Marketing Manager by providing you with a solid foundation in recommender systems. Recommender systems are used by companies like Amazon and Netflix to personalize the user experience and increase sales. By understanding how recommender systems work, you will be able to better understand how to develop marketing campaigns that reach the right customers.
Business Analyst
Business Analyst
Business Analysts work with businesses to improve their operations. They analyze data, identify problems, and develop solutions. This course can help you develop the skills necessary to become a successful Business Analyst by providing you with a solid foundation in recommender systems. Recommender systems are used by companies like Amazon and Netflix to personalize the user experience and increase sales. By understanding how recommender systems work, you will be able to better understand how to analyze data and identify problems.
Software Engineer
Software Engineer
Software Engineers design, develop, and maintain software applications. They work closely with product managers and designers to bring new products to market. This course can help you develop the skills necessary to become a successful Software Engineer by providing you with a solid foundation in recommender systems. Recommender systems are used by companies like Amazon and Netflix to personalize the user experience and increase sales. By understanding how recommender systems work, you will be able to better understand how to design and develop software applications that meet the needs of customers.
Data Scientist
Data Scientist
Data Scientists use data to solve business problems. They work closely with data analysts and engineers to build models and develop solutions. This course can help you develop the skills necessary to become a successful Data Scientist by providing you with a solid foundation in recommender systems. Recommender systems are used by companies like Amazon and Netflix to personalize the user experience and increase sales. By understanding how recommender systems work, you will be able to better understand how to use data to solve business problems.
User Experience Designer
User Experience Designer
User Experience Designers create products that are easy to use and enjoyable. They work closely with engineers and product managers to ensure that products meet the needs of users. This course can help you develop the skills necessary to become a successful User Experience Designer by providing you with a solid foundation in recommender systems. Recommender systems are used by companies like Amazon and Netflix to personalize the user experience and increase sales. By understanding how recommender systems work, you will be able to better understand how to design products that users love.
Information Architect
Information Architect
Information Architects design and organize websites and other digital products. They work closely with user experience designers and engineers to ensure that products are easy to find and use. This course can help you develop the skills necessary to become a successful Information Architect by providing you with a solid foundation in recommender systems. Recommender systems are used by companies like Amazon and Netflix to personalize the user experience and increase sales. By understanding how recommender systems work, you will be able to better understand how to design and organize products that are easy to find and use.
Technical Writer
Technical Writer
Technical Writers create documentation for software and other technical products. They work closely with engineers and product managers to ensure that documentation is accurate and easy to understand. This course can help you develop the skills necessary to become a successful Technical Writer by providing you with a solid foundation in recommender systems. Recommender systems are used by companies like Amazon and Netflix to personalize the user experience and increase sales. By understanding how recommender systems work, you will be able to better understand how to write documentation that is clear and concise.
Instructional Designer
Instructional Designer
Instructional Designers create and develop educational materials. They work closely with teachers and students to ensure that materials are effective and engaging. This course can help you develop the skills necessary to become a successful Instructional Designer by providing you with a solid foundation in recommender systems. Recommender systems are used by companies like Amazon and Netflix to personalize the user experience and increase sales. By understanding how recommender systems work, you will be able to better understand how to create and develop educational materials that are effective and engaging.
Librarian
Librarian
Librarians help people find and use information. They work in libraries, schools, and other organizations. This course may be useful for Librarians by providing them with a solid foundation in recommender systems. Recommender systems are used by companies like Amazon and Netflix to personalize the user experience and increase sales. By understanding how recommender systems work, Librarians will be able to better understand how to help people find and use information.
Archivist
Archivist
Archivists preserve and manage historical records. They work in libraries, museums, and other organizations. This course may be useful for Archivists by providing them with a solid foundation in recommender systems. Recommender systems are used by companies like Amazon and Netflix to personalize the user experience and increase sales. By understanding how recommender systems work, Archivists will be able to better understand how to preserve and manage historical records.
Museum curator
Museum curator
Museum Curators oversee the collections of museums. They work with other staff to develop exhibits and educational programs. This course may be useful for Museum Curators by providing them with a solid foundation in recommender systems. Recommender systems are used by companies like Amazon and Netflix to personalize the user experience and increase sales. By understanding how recommender systems work, Museum Curators will be able to better understand how to develop exhibits and educational programs that are engaging and informative.
Historian
Historian
Historians study the past. They work in universities, museums, and other organizations. This course may be useful for Historians by providing them with a solid foundation in recommender systems. Recommender systems are used by companies like Amazon and Netflix to personalize the user experience and increase sales. By understanding how recommender systems work, Historians will be able to better understand how to research and interpret the past.
Teacher
Teacher
Teachers educate students. They work in schools, colleges, and other educational institutions. This course may be useful for Teachers by providing them with a solid foundation in recommender systems. Recommender systems are used by companies like Amazon and Netflix to personalize the user experience and increase sales. By understanding how recommender systems work, Teachers will be able to better understand how to create and deliver educational content that is engaging and effective.
For more career information including salaries, visit:
OpenCourser.com/course/95mol5/introduction
Reading list
We've selected ten 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
Introduction to Recommender Systems: Non-Personalized and Content-Based.
This comprehensive handbook provides a thorough overview of the field of recommender systems, covering a wide range of topics such as recommendation algorithms, evaluation methods, and applications in various domains.
Save
Provides a comprehensive overview of recommender system algorithms and their applications in various domains, including e-commerce, entertainment, and healthcare.
Provides a comprehensive overview of content-based filtering, a technique used in recommender systems to make recommendations based on the content of items. It covers various algorithms and techniques, and good resource for anyone who wants to understand this topic.
This textbook provides a comprehensive foundation in recommender systems, covering topics such as data mining, machine learning, and evaluation methods.
This textbook provides a comprehensive overview of recommender systems, covering various techniques and applications. It good resource for anyone who wants to learn about this field.
Provides a comprehensive overview of information retrieval, which related field to recommender systems. It covers various techniques and algorithms, and good resource for anyone who wants to understand this topic.
Save
Provides a comprehensive overview of machine learning, which fundamental topic in recommender systems. It covers various algorithms and techniques, and good resource for anyone who wants to understand this topic.
Save
Provides a comprehensive overview of data mining, which related field to recommender systems. It covers various techniques and algorithms, and good resource for anyone who wants to understand this topic.
Provides a overview of statistical methods used in recommender systems. It covers various algorithms and techniques, and good resource for anyone who wants to understand this topic.
Provides a practical guide to building and deploying recommender systems. It covers various techniques and best practices, and good resource for anyone who wants to implement recommender systems.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/95mol5/introduction
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Effort
Approx. 23 hours
Level
Intermediate
Via
Coursera
Institution
University of Minnesota
Instructors
Joseph A Konstan
Michael D. Ekstrand
Language
English
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This course belongs to a
collection
called
Recommender Systems. It's comprised of five courses:
- Introduction to Recommender Systems: Non-Personalized and Content-Based (viewing)
- Nearest Neighbor Collaborative Filtering
- Recommender Systems Capstone
- Matrix Factorization and Advanced Techniques
- Recommender Systems: Evaluation and Metrics
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