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Recommender Systems Capstone

Michael D. Ekstrand and Joseph A Konstan

This capstone project course for the Recommender Systems Specialization brings together everything you've learned about recommender systems algorithms and evaluation into a comprehensive recommender analysis and design project. You will be given a case study to complete where you have to select and justify the design of a recommender system through analysis of recommender goals and algorithm performance.

Learners in the honors track will focus on experimental evaluation of the algorithms against medium sized datasets. The standard track will include a mix of provided results and spreadsheet exploration.

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This capstone project course for the Recommender Systems Specialization brings together everything you've learned about recommender systems algorithms and evaluation into a comprehensive recommender analysis and design project. You will be given a case study to complete where you have to select and justify the design of a recommender system through analysis of recommender goals and algorithm performance.

Learners in the honors track will focus on experimental evaluation of the algorithms against medium sized datasets. The standard track will include a mix of provided results and spreadsheet exploration.

Both groups will produce a capstone report documenting the analysis, the selected solution, and the justification for that solution.

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What's inside

Syllabus

Traffic lights

Read about what's good
what should give you pause
and possible dealbreakers
Suitable for individuals with prior experience in recommender systems or a related field seeking to enhance their understanding and skills
Taught by instructors with extensive knowledge and experience in recommender systems research and industry applications
Provides a comprehensive overview of recommender systems algorithms, evaluation techniques, and practical considerations
Develops critical thinking and analytical skills through a project-based approach involving analysis and design of recommender systems
Offers two tracks (standard and honors) to cater to varying levels of learner experience and interest

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Reviews summary

Apply recommender systems skills in capstone

According to learners, this capstone course provides a practical opportunity to apply recommender systems concepts learned throughout the specialization. Many students found the main project to be challenging, requiring significant effort and time, but ultimately felt it was rewarding and a great culmination of the specialization. Some reviewers noted that the project instructions or evaluation criteria could be clearer, leading to uncertainty. Experiences with instructor and TA support varied, with some wishing for quicker or more detailed responses. Overall, learners emphasize that it requires a solid foundation from prior courses in the specialization.
Requires prior knowledge from other courses.
"Definitely take all the prerequisite courses before attempting this capstone; it builds directly on them."
"You really need to have a solid understanding of the algorithms from the previous courses."
"Assumes you are comfortable with the material covered earlier in the specialization."
"Not recommended as a standalone course."
Hands-on application of skills learned previously.
"This project was a great way to bring together everything we learned and apply it in a real-world context. Highly valuable."
"The practical application of the concepts in the capstone project was the most beneficial part for me."
"Loved getting my hands dirty with a realistic recommender system problem."
"I was able to apply the algorithms and evaluation techniques directly."
Experiences with instructor/TA support differ.
"Found forum responses to be slow at times when I was stuck on the project."
"Support for complex technical issues on the project was sometimes inadequate."
"The quality and speed of TA support varied throughout the course."
"Could have used more guidance during the implementation phase."
Project requirements or evaluation criteria lack clarity.
"Some aspects of the project instructions were vague, leading to confusion about requirements."
"It felt unclear exactly what was expected for certain parts of the project write-up..."
"Grading criteria weren't always transparent, which made it hard to know how to improve."
"Wish the project prompt had been more specific from the start."
The main project requires significant time and effort.
"The project was much more challenging than I anticipated and required a lot of time."
"Struggled quite a bit with implementing and debugging the models for the project."
"This is a serious capstone that demands dedication, be prepared to spend significant hours."
"The difficulty level jumped significantly for the final project."

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 Recommender Systems Capstone with these activities:
Refresh your statistics skills
Review the basics of statistics to strengthen your understanding of the mathematical foundations of recommender systems.
Browse courses on Statistical Analysis
Show steps
  • Review your lecture notes or textbooks
  • Complete practice problems
  • Attend a refresher course or workshop
  • Join a study group or online forum for support
Refresh your knowledge of machine learning
Review the fundamentals of machine learning to build upon your prior knowledge and enhance your understanding of recommender systems.
Browse courses on Machine Learning Algorithms
Show steps
  • Review your lecture notes or textbooks
  • Complete practice problems
  • Attend a refresher course or workshop
  • Join a study group or online forum for support
Explore online recommender systems tutorials
Review the concepts and techniques of recommender systems to build a strong foundation before the course starts.
Browse courses on Recommender Systems
Show steps
  • Search for online tutorials on recommender systems
  • Review the topics covered in each tutorial
  • Identify the tutorials most suited to your learning style and goals
  • Complete at least two tutorials before the course begins
Five other activities
Expand to see all activities and additional details
Show all eight activities
Start a mini recommender system project
Apply the concepts learned in the course to a practical project, solidifying your understanding and gaining hands-on experience.
Browse courses on Project-Based Learning
Show steps
  • Define the scope and goals of your project
  • Choose a dataset and algorithms to use
  • Develop a prototype of your recommender system
  • Iterate on your design and improve the performance
  • Document your project and share your findings
Create a blog post or article on a recommender systems topic
Deepen your understanding of a specific recommender systems topic by researching and writing about it, improving your communication and critical thinking skills.
Show steps
  • Choose a topic that interests you
  • Research the topic thoroughly
  • Organize your thoughts and create an outline
  • Write the blog post or article
  • Share your work with others
Participate in a recommender systems competition
Challenge yourself by participating in a competition, gaining valuable experience and insights into the field of recommender systems.
Show steps
  • Find a suitable competition
  • Form a team or work individually
  • Develop a solution to the competition problem
  • Submit your solution and compete with others
Contribute to an open-source recommender systems project
Gain practical experience and contribute to the wider recommender systems community by working on an open-source project.
Show steps
  • Find a suitable open-source project
  • Review the project's documentation and codebase
  • Identify an area where you can contribute
  • Make a pull request with your contribution
Mentor others in the field of recommender systems
Reinforce your own understanding by sharing your knowledge and helping others learn about recommender systems.
Show steps
  • Identify opportunities to mentor others
  • Prepare materials and resources
  • Provide guidance and support
  • Reflect on your mentoring experience

Career center

Learners who complete Recommender Systems Capstone will develop knowledge and skills that may be useful to these careers:
Data Analyst
Data Analysts analyze data to extract meaningful insights and trends, which can be used to make informed decisions in a variety of industries. Recommender Systems Capstone is a course that teaches learners how to design and evaluate recommender systems, which are algorithms that make personalized recommendations to users. This course provides a strong foundation in data analysis and data mining, which are essential skills for Data Analysts. Additionally, the course's focus on experimental evaluation of algorithms can help Data Analysts develop the skills necessary to evaluate the performance of their own algorithms.
See salaries and explore the career path for Data Analyst
Information Technology Manager
Information Technology (IT) Managers are responsible for planning, installing, and maintaining computer systems within an organization. Recommender Systems Capstone is a course that teaches learners how to design and evaluate recommender systems, which are algorithms that make personalized recommendations to users. This course provides IT Managers with the skills necessary to understand and implement recommender systems within their own organizations, which can help them improve customer satisfaction and engagement.
See salaries and explore the career path for Information Technology Manager
Software Developer
Software Developers design, develop, and maintain software applications. Recommender Systems Capstone is a course that teaches learners how to design and evaluate recommender systems, which are algorithms that make personalized recommendations to users. This course provides Software Developers with the skills necessary to develop recommender systems for a variety of applications, which can help them improve user experience and engagement.
See salaries and explore the career path for Software Developer
Machine Learning Engineer
Machine Learning Engineers design and develop machine learning models to solve complex problems. Recommender Systems Capstone is a course that teaches learners how to design and evaluate recommender systems, which are algorithms that make personalized recommendations to users. This course provides Machine Learning Engineers with the skills necessary to develop recommender systems using machine learning techniques, which can help them improve the accuracy and efficiency of their models.
See salaries and explore the career path for Machine Learning Engineer
Data Scientist
Data Scientists use data to solve complex problems and make informed decisions. Recommender Systems Capstone is a course that teaches learners how to design and evaluate recommender systems, which are algorithms that make personalized recommendations to users. This course provides Data Scientists with the skills necessary to develop and evaluate recommender systems for a variety of applications, which can help them improve decision-making and customer engagement.
See salaries and explore the career path for Data Scientist
Product Manager
Product Managers are responsible for managing the development of new products. Recommender Systems Capstone is a course that teaches learners how to design and evaluate recommender systems, which are algorithms that make personalized recommendations to users. This course provides Product Managers with the skills necessary to understand the benefits and limitations of recommender systems, and how to use them to improve product development. Additionally, the course's focus on experimental evaluation of algorithms can help Product Managers develop the skills necessary to evaluate the performance of their own products.
See salaries and explore the career path for Product Manager
Business Analyst
Business Analysts use data to understand and improve business performance. Recommender Systems Capstone is a course that teaches learners how to design and evaluate recommender systems, which are algorithms that make personalized recommendations to users. This course provides Business Analysts with the skills necessary to use recommender systems to improve customer engagement and satisfaction.
See salaries and explore the career path for Business Analyst
Marketing Analyst
Marketing Analysts use data to understand customer behavior and improve marketing campaigns. Recommender Systems Capstone is a course that teaches learners how to design and evaluate recommender systems, which are algorithms that make personalized recommendations to users. This course provides Marketing Analysts with the skills necessary to use recommender systems to improve the effectiveness of their marketing campaigns.
See salaries and explore the career path for Marketing Analyst
Consultant
Consultants provide advice and expertise to businesses and organizations. Recommender Systems Capstone is a course that teaches learners how to design and evaluate recommender systems, which are algorithms that make personalized recommendations to users. This course provides Consultants with the skills necessary to understand the benefits and limitations of recommender systems, and how to use them to improve business performance. Additionally, the course's focus on experimental evaluation of algorithms can help Consultants develop the skills necessary to evaluate the performance of their own recommendations.
See salaries and explore the career path for Consultant
Project Manager
Project Managers plan and manage projects to ensure their successful completion. Recommender Systems Capstone is a course that teaches learners how to design and evaluate recommender systems, which are algorithms that make personalized recommendations to users. This course provides Project Managers with the skills necessary to understand the benefits and limitations of recommender systems, and how to use them to improve project outcomes. Additionally, the course's focus on experimental evaluation of algorithms can help Project Managers develop the skills necessary to evaluate the performance of their own projects.
See salaries and explore the career path for Project Manager
Statistician
Statisticians use data to analyze and interpret trends. Recommender Systems Capstone is a course that teaches learners how to design and evaluate recommender systems, which are algorithms that make personalized recommendations to users. This course provides Statisticians with the skills necessary to understand the benefits and limitations of recommender systems, and how to use them to improve data analysis and interpretation. Additionally, the course's focus on experimental evaluation of algorithms can help Statisticians develop the skills necessary to evaluate the performance of their own analysis.
See salaries and explore the career path for Statistician
UX Designer
UX Designers design user interfaces and experiences for websites and applications. Recommender Systems Capstone is a course that teaches learners how to design and evaluate recommender systems, which are algorithms that make personalized recommendations to users. This course provides UX Designers with the skills necessary to understand the benefits and limitations of recommender systems, and how to use them to improve the user experience of their own designs.
See salaries and explore the career path for UX Designer
Web Developer
Web Developers design and develop websites. Recommender Systems Capstone is a course that teaches learners how to design and evaluate recommender systems, which are algorithms that make personalized recommendations to users. This course provides Web Developers with the skills necessary to understand the benefits and limitations of recommender systems, and how to use them to improve the user experience of their own websites.
See salaries and explore the career path for Web Developer
Data Entry Clerk
Data Entry Clerks enter data into computer systems. Recommender Systems Capstone is a course that teaches learners how to design and evaluate recommender systems, which are algorithms that make personalized recommendations to users. This course may be helpful for Data Entry Clerks who want to learn more about data analysis and data mining.
See salaries and explore the career path for Data Entry Clerk
Software Tester
Software Testers test software applications to find bugs and ensure that they work properly. Recommender Systems Capstone is a course that teaches learners how to design and evaluate recommender systems, which are algorithms that make personalized recommendations to users. This course may be helpful for Software Testers who want to learn more about data analysis and data mining.
See salaries and explore the career path for Software Tester

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 Recommender Systems Capstone.
Cover image
Recommender Systems Handbook
Save
This comprehensive handbook provides a thorough overview of recommender systems, covering various algorithms, techniques, and applications. It serves as an excellent reference for understanding the fundamentals and advancements in the field.
Recommender Systems Handbook
Kindle Edition
$$$$
Cover image
Recommender Systems
Save
Explores the application of deep learning techniques in recommender systems. It presents advanced concepts and case studies, providing insights into the latest advancements and potential of deep learning in this domain.
Recommender Systems: An Introduction
Hardcover
$$$
Recommender Systems: An Introduction
Kindle Edition
$$$
Cover image
Recommender Systems
Save
Offers a gentle introduction to recommender systems. It provides a comprehensive overview of the field, covering the history, techniques, and applications of recommender systems, making it suitable for beginners seeking a foundational understanding.
Recommender Systems
Paperback
Check
Recommender Systems
Kindle Edition
Check
Cover image
Reviews in Recommender Systems: 2022
Save
Focuses on the practical aspects of building and deploying recommender systems. It provides hands-on guidance, case studies, and real-world examples, making it valuable for practitioners seeking to implement and maintain effective recommender systems.
Reviews in Recommender Systems: 2022
Paperback
Check
Reviews in Recommender Systems: 2022
Kindle Edition
Check
Cover image
Collaborative Filtering Recommender Systems
Save
Explores adaptive recommender systems, which can adapt to changing user preferences and contexts. It covers various adaptation techniques, evaluation methods, and case studies, providing insights into the latest advancements in this area.
Collaborative Filtering Recommender Systems...
Paperback
$$$
Cover image
Machine Learning for Text
Save
Focuses on the challenges and techniques involved in building recommender systems for large-scale datasets. It discusses distributed computing, scalability, and efficiency considerations, offering valuable insights for practitioners dealing with real-world data.
Machine Learning for Text
Paperback
Check
Machine Learning for Text
Kindle Edition
Check

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Course image
Effort
1-3 weeks of study, 3-5 hours per week
Via
Coursera
Institution
University of Minnesota
Instructors
Michael D. Ekstrand
Joseph A Konstan
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 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
  2. Nearest Neighbor Collaborative Filtering
  3. Recommender Systems Capstone (viewing)
  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
Suitable for individuals with prior experience in recommender systems or a related field seeking to enhance their understanding and skills
Taught by instructors with extensive knowledge and experience in recommender systems research and industry applications
Provides a comprehensive overview of recommender systems algorithms, evaluation techniques, and practical considerations
Develops critical thinking and analytical skills through a project-based approach involving analysis and design of recommender systems
Offers two tracks (standard and honors) to cater to varying levels of learner experience and interest
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