Introduction to Recommender Systems: Non-Personalized and Content-Based from Coursera

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.

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.

Introducing Recommender Systems

This module introduces recommender systems in more depth. It includes a detailed taxonomy of the types of recommender systems, and also includes tours of two systems heavily dependent on recommender technology: MovieLens and Amazon.com. There is an introductory assessment in the final lesson to ensure that you understand the core concepts behind recommendations before we start learning how to compute them.

Non-Personalized and Stereotype-Based Recommenders

In this module, you will learn several techniques for non- and lightly-personalized recommendations, including how to use meaningful summary statistics, how to compute product association recommendations, and how to explore using demographics as a means for light personalization. There is both an assignment (trying out these techniques in a spreadsheet) and a quiz to test your comprehension.

Content-Based Filtering -- Part I

The next topic in this course is content-based filtering, a technique for personalization based on building a profile of personal interests. Divided over two weeks, you will learn and practice the basic techniques for content-based filtering and then explore a variety of advanced interfaces and content-based computational techniques being used in recommender systems.

Content-Based Filtering -- Part II

The assessments for content-based filtering include an assignment where you compute three types of profile and prediction using a spreadsheet and a quiz on the topics covered. The assignment is in three parts -- a written assignment, a video intro, and a "quiz" where you provide answers from your work to be automatically graded.

Course Wrap-up

We close this course with a set of mathematical notation that will be helpful as we move forward into a wider range of recommender systems (in later courses in this specialization).

Good to know

Know what's good

, what to watch for

, 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

Reviews summary

Intro to recommender systems: non-personalized and content-based

Learners say this is a good introductory course to recommender systems but that the video lectures are too long. The assignments are very interesting and the course covers all the basics. However, many learners wish there was more programming involved and more hands-on work, especially since this is an introductory course and some may already be familiar with the basics.

Assignments are very interesting and help understand concepts.

"Exercises are very interesting and help me understand the concepts"

"In its current on-demand form it suffers from a number of problems in execution."

"Programming exercises have been reformulated to be doable in Excel without relying on R or Matlab (I imagine 99% of serious students still end up using R, Matlab or Python regardless, so not sure what their goal is here), so they are incredibly simple."

Lectures are very long with low information density.

"lectures are too long, but the information density is low."

"lectures are too long as they could be shortened to learn the same."

"Extremely long lectures with low information density."

This is still the best intro to recommender systems available, but it is too basic.

"This is probably still the best introduction to recommender systems available, better than some of the textbooks that have been written on the topic."

"but it does an excellent job of covering the basic topics and providing pointers for further study."

"The amount of technical coverage is low, and the nontechnical sections are so long that I wish I had a x4 playback speed option or could just read the transcript and move on."

The programming assignments do not make use of R or Python or Matlab; they are done in Excel.

"However in its current on-demand form it suffers from a number of problems in execution."

"The programming assignments do not make use of the coursera unit-testing grader and simply ask you to manually fill in the recommender results, this is both time consuming and non-informative when you get it wrong"

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

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Expand knowledge of recommender systems beyond the course materials

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Identify advanced recommender systems techniques
Find online tutorials or courses on these techniques
Follow the tutorials and implement the techniques

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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.

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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.

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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.

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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.

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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.

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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.

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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.

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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.

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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.

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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.

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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.

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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.

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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.

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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.

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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.

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