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Introduction to Recommender Systems

Non-Personalized and Content-Based

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.

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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.

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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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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
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

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 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.
See salaries and explore the career path for Data Analyst
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.
See salaries and explore the career path for Product 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.
See salaries and explore the career path for Marketing Manager
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.
See salaries and explore the career path for Business Analyst
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.
See salaries and explore the career path for Software Engineer
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.
See salaries and explore the career path for Data Scientist
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.
See salaries and explore the career path for User Experience Designer
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.
See salaries and explore the career path for Information Architect
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.
See salaries and explore the career path for Technical Writer
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.
See salaries and explore the career path for Instructional Designer
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.
See salaries and explore the career path for Librarian
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.
See salaries and explore the career path for Archivist
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.
See salaries and explore the career path for Museum curator
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.
See salaries and explore the career path for Historian
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.
See salaries and explore the career path for Teacher

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.
Cover image
Recommender Systems Handbook
Save
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.
Recommender Systems Handbook
Kindle Edition
$$$$
Cover image
Recommender Systems
Save
Provides a comprehensive overview of recommender system algorithms and their applications in various domains, including e-commerce, entertainment, and healthcare.
Recommender Systems: An Introduction
Hardcover
$$$
Recommender Systems: An Introduction
Kindle Edition
$$$
Cover image
Collaborative Filtering Recommender Systems
Save
Provides a comprehensive overview of content-based filtering, a technique used in recommender systems to make recommendations based on the content of items. It covers various algorithms and techniques, and good resource for anyone who wants to understand this topic.
Collaborative Filtering Recommender Systems...
Paperback
$$$
Cover image
Neural Networks and Deep Learning
Save
This textbook provides a comprehensive foundation in recommender systems, covering topics such as data mining, machine learning, and evaluation methods.
Neural Networks and Deep Learning: A Textbook
Hardcover
$$
Neural Networks and Deep Learning: A Textbook
Hardcover
$$$
(中文) Neural network and deep learning(Chinese Edition)
Paperback
$$
Neural Networks and Deep Learning: A Textbook
Kindle Edition
$$
Cover image
Neural Networks and Deep Learning
Save
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.
Neural Networks and Deep Learning: A Textbook
Hardcover
$$
Neural Networks and Deep Learning: A Textbook
Kindle Edition
$$
(中文) Neural network and deep learning(Chinese Edition)
Paperback
$$
Introduction to Information Retrieval
Save
Provides a comprehensive overview of information retrieval, which related field to recommender systems. It covers various techniques and algorithms, and good resource for anyone who wants to understand this topic.
Introduction to Information Retrieval
Hardcover
$$$
Introduction to Information Retrieval
Kindle Edition
$$$
Machine Learning
Save
Provides a comprehensive overview of machine learning, which fundamental topic in recommender systems. It covers various algorithms and techniques, and good resource for anyone who wants to understand this topic.
Machine Learning (McGraw-Hill International...
Paperback
$$$
Data Mining
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.
Data Mining: Concepts and Techniques (The Morgan...
Kindle Edition
$$$
Data Mining: Concepts and Techniques (The Morgan...
Hardcover
$$$
Data Mining: Concepts and Techniques, Second...
Hardcover
$$
Data Mining: Know It All
Hardcover
$$$
Cover image
Recommender Systems for Social Tagging Systems
Save
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.
Recommender Systems for Social Tagging Systems...
Kindle Edition
$$$
Cover image
Neural Networks and Deep Learning
Save
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.
Neural Networks and Deep Learning
Paperback
Check
Neural Networks and Deep Learning
Kindle Edition
Check

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Course image
Effort
Approx. 23 hours
Level
Intermediate
Via
Coursera
Institution
University of Minnesota
Instructors
Joseph A Konstan
Michael D. Ekstrand
Language
English
Transcripts
Español Français Português Deutsch Italiano 中文 العربية 한국어 日本語 हिंदी Nederlands Svenska Pусский Türkçe Polski Ελληνικά українська мова magyar nyelv Bahasa Indonesia ไทย қазақша اُردُو, فارسی বাংলা tiếng Việt پښتو آذربایجان دیلی
This course belongs to a collection called Recommender Systems. It's comprised of five courses:
  1. Introduction to Recommender Systems: Non-Personalized and Content-Based (viewing)
  2. Nearest Neighbor Collaborative Filtering
  3. Recommender Systems Capstone
  4. Matrix Factorization and Advanced Techniques
  5. Recommender Systems: Evaluation and Metrics
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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