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Michael D. Ekstrand
and
Joseph A Konstan
In this course you will learn how to evaluate recommender systems. You will gain familiarity with several families of metrics, including ones to measure prediction accuracy, rank accuracy, decision-support, and other factors such as diversity, product coverage, and serendipity. You will learn how different metrics relate to different user goals and business goals. You will also learn how to rigorously conduct offline evaluations (i.e., how to prepare and sample data, and how to aggregate results). And you will learn about online (experimental) evaluation. At the completion of this course you will have the tools you need to compare different recommender system alternatives for a wide variety of uses.
Register for this course and see more details by visiting:
OpenCourser.com/course/ixsdph/recommender
What's inside
Syllabus
Preface
Basic Prediction and Recommendation Metrics
Advanced Metrics and Offline Evaluation
Read more
Syllabus
Preface
Basic Prediction and Recommendation Metrics
Advanced Metrics and Offline Evaluation
Read more
Online Evaluation
Evaluation Design
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Teaches a variety of metrics to evaluate recommender systems for different user goals and business goals
Develops skills in offline and online evaluation of recommender systems
Provides tools to compare different recommender system alternatives
Instructors are recognized for their work in the field of recommender systems
Provides both foundational and advanced knowledge in recommender system evaluation
May require some background knowledge in recommender systems or machine learning
Save this course
Save Recommender Systems: Evaluation and Metrics to your list so you can find it easily later:
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Reviews summary
Practical offline evaluation
Learners say this course provides practical offline evaluation in Recommender Systems, but more practice with online evaluation would make this course more valuable.
Offline evaluations are practical.
"the part of offline evaluation is really good and practical as well."
The course lacks practice with online evaluation.
"However, although knowing online evaluation is a more complex subject, I felt it lacked a little bit how to put all this knowledge in practice."
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: Evaluation and Metrics with these
activities:
Review metrics
Show steps
Refresh your knowledge of basic evaluation metrics before starting the course
Browse courses on
Metrics
Show steps
-
Re-familiarize yourself with the definition of precision and recall.
-
Review the calculation formula for accuracy.
-
Consider the trade-offs between precision and recall.
Review statistical concepts
Show steps
Brush up on fundamental statistical concepts to strengthen your understanding of recommender system evaluation techniques.
Browse courses on
Statistics
Show steps
-
Review textbooks or online resources on introductory statistics.
-
Practice solving statistical problems and exercises.
Practice calculating metrics
Show steps
Practice applying different evaluation metrics to real-world scenarios
Browse courses on
Metrics
Show steps
-
Calculate the precision and recall of a recommender system using a provided dataset.
-
Compare the accuracy of two different recommender system models.
-
Evaluate the performance of a recommender system on a variety of metrics.
Eight other activities
Expand to see all activities and additional details
Show all 11 activities
Follow industry blogs and research papers on recommender systems
Show steps
Stay abreast of the latest advancements and best practices in recommender systems by actively seeking out and reviewing relevant content.
Browse courses on
Recommender Systems
Show steps
-
Identify reputable industry blogs and research paper repositories.
-
Subscribe to blogs and set up alerts for new content.
-
Read and review articles regularly to stay informed about emerging trends.
Connect with experts in recommender systems
Show steps
Identify and reach out to individuals with experience and expertise in recommender systems to gain valuable insights and guidance.
Browse courses on
Recommender Systems
Show steps
-
Identify potential mentors through professional networks or online platforms.
-
Craft a personalized message expressing your interest and qualifications.
-
Schedule informational interviews to learn from their experiences and insights.
Design a recommender system evaluation plan
Show steps
Develop a comprehensive plan to evaluate the effectiveness of a recommender system in a specific context.
Browse courses on
Evaluation Plan
Show steps
-
Define the evaluation goals and objectives.
-
Identify relevant evaluation metrics and data sources.
-
Design experimental or observational studies to collect data.
-
Analyze the results and draw conclusions about the recommender system's performance.
Attend a workshop on recommender system evaluation
Show steps
Gain exposure to different perspectives and learn from experts in the field
Browse courses on
Recommender Systems
Show steps
-
Research and find a workshop on recommender system evaluation.
-
Register for the workshop and make arrangements to attend.
-
Actively participate in the workshop and take notes.
-
Follow up with the workshop organizers or speakers to ask questions or learn more.
Create a tutorial on a recommender metric
Show steps
Enhance your understanding of recommender metrics by creating a guide to explain one to others.
Show steps
-
Choose a recommender metric to focus on.
-
Research the metric and gather necessary information.
-
Create an outline for the tutorial.
-
Write the tutorial content.
-
Proofread and finalize the tutorial.
Build a simple recommender system
Show steps
Apply your knowledge by designing and implementing a working recommender system that addresses a specific problem or use case.
Browse courses on
Recommender Systems
Show steps
-
Define the problem or use case for your recommender system.
-
Gather and prepare the necessary data.
-
Choose appropriate algorithms and evaluation metrics.
-
Build and train your recommender system.
-
Evaluate and refine your system based on the chosen metrics.
Write a blog post about recommender system evaluation
Show steps
Solidify your understanding of recommender system evaluation by explaining it to others
Browse courses on
Evaluation
Show steps
-
Choose a specific aspect of recommender system evaluation to focus on.
-
Research the topic and gather relevant information.
-
Write a blog post that clearly explains the concept and its importance.
-
Share your blog post with others and get feedback.
Participate in a recommender system evaluation competition
Show steps
Challenge yourself by applying your knowledge and skills in a competitive environment
Browse courses on
Recommender Systems
Show steps
-
Find a recommender system evaluation competition that aligns with your interests.
-
Form a team or work individually to develop a solution.
-
Submit your solution and participate in the competition.
-
Review the results and learn from your experience.
Review metrics
Show steps
Refresh your knowledge of basic evaluation metrics before starting the course
Browse courses on
Metrics
Show steps
Show steps
Show steps
- Re-familiarize yourself with the definition of precision and recall.
- Review the calculation formula for accuracy.
- Consider the trade-offs between precision and recall.
Review statistical concepts
Show steps
Brush up on fundamental statistical concepts to strengthen your understanding of recommender system evaluation techniques.
Browse courses on
Statistics
Show steps
Show steps
Show steps
- Review textbooks or online resources on introductory statistics.
- Practice solving statistical problems and exercises.
Practice calculating metrics
Show steps
Practice applying different evaluation metrics to real-world scenarios
Browse courses on
Metrics
Show steps
Show steps
Show steps
- Calculate the precision and recall of a recommender system using a provided dataset.
- Compare the accuracy of two different recommender system models.
- Evaluate the performance of a recommender system on a variety of metrics.
Eight other activities
Expand to see all activities and additional details
Show all 11 activities
Follow industry blogs and research papers on recommender systems
Show steps
Stay abreast of the latest advancements and best practices in recommender systems by actively seeking out and reviewing relevant content.
Browse courses on
Recommender Systems
Show steps
Show steps
Show steps
- Identify reputable industry blogs and research paper repositories.
- Subscribe to blogs and set up alerts for new content.
- Read and review articles regularly to stay informed about emerging trends.
Connect with experts in recommender systems
Show steps
Identify and reach out to individuals with experience and expertise in recommender systems to gain valuable insights and guidance.
Browse courses on
Recommender Systems
Show steps
Show steps
Show steps
- Identify potential mentors through professional networks or online platforms.
- Craft a personalized message expressing your interest and qualifications.
- Schedule informational interviews to learn from their experiences and insights.
Design a recommender system evaluation plan
Show steps
Develop a comprehensive plan to evaluate the effectiveness of a recommender system in a specific context.
Browse courses on
Evaluation Plan
Show steps
Show steps
Show steps
- Define the evaluation goals and objectives.
- Identify relevant evaluation metrics and data sources.
- Design experimental or observational studies to collect data.
- Analyze the results and draw conclusions about the recommender system's performance.
Attend a workshop on recommender system evaluation
Show steps
Gain exposure to different perspectives and learn from experts in the field
Browse courses on
Recommender Systems
Show steps
Show steps
Show steps
- Research and find a workshop on recommender system evaluation.
- Register for the workshop and make arrangements to attend.
- Actively participate in the workshop and take notes.
- Follow up with the workshop organizers or speakers to ask questions or learn more.
Create a tutorial on a recommender metric
Show steps
Enhance your understanding of recommender metrics by creating a guide to explain one to others.
Show steps
Show steps
Show steps
- Choose a recommender metric to focus on.
- Research the metric and gather necessary information.
- Create an outline for the tutorial.
- Write the tutorial content.
- Proofread and finalize the tutorial.
Build a simple recommender system
Show steps
Apply your knowledge by designing and implementing a working recommender system that addresses a specific problem or use case.
Browse courses on
Recommender Systems
Show steps
Show steps
Show steps
- Define the problem or use case for your recommender system.
- Gather and prepare the necessary data.
- Choose appropriate algorithms and evaluation metrics.
- Build and train your recommender system.
- Evaluate and refine your system based on the chosen metrics.
Write a blog post about recommender system evaluation
Show steps
Solidify your understanding of recommender system evaluation by explaining it to others
Browse courses on
Evaluation
Show steps
Show steps
Show steps
- Choose a specific aspect of recommender system evaluation to focus on.
- Research the topic and gather relevant information.
- Write a blog post that clearly explains the concept and its importance.
- Share your blog post with others and get feedback.
Participate in a recommender system evaluation competition
Show steps
Challenge yourself by applying your knowledge and skills in a competitive environment
Browse courses on
Recommender Systems
Show steps
Show steps
Show steps
- Find a recommender system evaluation competition that aligns with your interests.
- Form a team or work individually to develop a solution.
- Submit your solution and participate in the competition.
- Review the results and learn from your experience.
Career center
Learners who complete Recommender Systems: Evaluation and Metrics will develop knowledge and skills
that may be useful to these careers:
Machine Learning Engineer
Machine Learning Engineer
Machine Learning Engineers design and build machine learning models. They work with data scientists to identify the most appropriate models for a given problem, and then they develop and implement these models. This course may be useful for Machine Learning Engineers as it provides a foundation in the evaluation of recommender systems. By understanding how to measure the effectiveness of these systems, Machine Learning Engineers can build more effective recommender systems.
Data Analyst
Data Analyst
Data Analysts collect, clean, and analyze data to identify trends and patterns. They use this information to make recommendations to businesses about how to improve their operations. This course may be useful for Data Analysts as it provides a foundation in the evaluation of recommender systems. By understanding how to measure the effectiveness of these systems, Data Analysts can make more informed recommendations about how to use them.
Data Scientist
Data Scientist
Data Scientists use data to solve business problems. They collect, clean, and analyze data to identify trends and patterns. This course may be useful for Data Scientists as it provides an overview of the different families of metrics used to evaluate recommender systems. By understanding these metrics, Data Scientists can develop more effective recommender systems.
Software Engineer
Software Engineer
Software Engineers design, develop, and test software applications. They work with product managers and other stakeholders to understand the requirements of a software application, and then they design and implement the application. This course may be useful for Software Engineers as it provides a foundation in the evaluation of recommender systems. By understanding how to measure the effectiveness of these systems, Software Engineers can develop more effective recommender systems.
Database Administrator
Database Administrator
Database Administrators manage the databases of organizations. They work with stakeholders to identify the data needs of the organization, and then they design and implement solutions to meet those needs. This course may be useful for Database Administrators as it provides a foundation in the evaluation of recommender systems. By understanding how to measure the effectiveness of these systems, Database Administrators can design and manage more effective databases.
Product Manager
Product Manager
Product Managers decide which features go into new products, and how the product is positioned. They work closely with engineering, marketing, and sales to turn ideas into successful products. This course may be useful for Product Managers as it helps build a foundation in evaluating recommender systems. By understanding how to measure the effectiveness of these systems, Product Managers can make more informed decisions about which ones to use and how to configure them.
Customer Success Manager
Customer Success Manager
Customer Success Managers help customers get the most value from a product or service. They work with customers to identify their needs and then develop and implement solutions to meet those needs. This course may be useful for Customer Success Managers as it provides a foundation in the evaluation of recommender systems. By understanding how to measure the effectiveness of these systems, Customer Success Managers can develop more effective strategies for helping customers get the most value from a product or service.
User Experience Designer
User Experience Designer
User Experience Designers design and evaluate the user experience of products and services. They work with product managers and other stakeholders to identify the needs of users, and then they design and implement solutions to meet those needs. This course may be useful for User Experience Designers as it provides a foundation in the evaluation of recommender systems. By understanding how to measure the effectiveness of these systems, User Experience Designers can design and evaluate more effective user experiences.
Interaction Designer
Interaction Designer
Interaction Designers design the interactions between users and products and services. They work with product managers and other stakeholders to identify the needs of users, and then they design and implement solutions to meet those needs. This course may be useful for Interaction Designers as it provides a foundation in the evaluation of recommender systems. By understanding how to measure the effectiveness of these systems, Interaction Designers can design and evaluate more effective interactions.
Information Architect
Information Architect
Information Architects design and organize the information architecture of websites and other digital products. They work with stakeholders to identify the information needs of users, and then they design and implement solutions to meet those needs. This course may be useful for Information Architects as it provides a foundation in the evaluation of recommender systems. By understanding how to measure the effectiveness of these systems, Information Architects can design and evaluate more effective information architectures.
Marketing Manager
Marketing Manager
Marketing Managers develop and execute marketing campaigns to promote products and services. They work with product managers and other stakeholders to identify the target audience for a marketing campaign, and then they develop and implement the campaign. This course may be useful for Marketing Managers as it provides a foundation in the evaluation of recommender systems. By understanding how to measure the effectiveness of these systems, Marketing Managers can develop more effective marketing campaigns.
Product Owner
Product Owner
Product Owners are responsible for the vision and execution of a product. They work with stakeholders to define the product vision, and then they lead the product team in developing and delivering the product. This course may be useful for Product Owners as it provides a foundation in the evaluation of recommender systems. By understanding how to measure the effectiveness of these systems, Product Owners can make more informed decisions about which ones to use and how to configure them.
Sales Manager
Sales Manager
Sales Managers lead and motivate sales teams to achieve sales goals. They work with marketing managers and other stakeholders to identify the target market for a product or service, and then they develop and implement sales strategies. This course may be useful for Sales Managers as it provides a foundation in the evaluation of recommender systems. By understanding how to measure the effectiveness of these systems, Sales Managers can develop more effective sales strategies.
Business Analyst
Business Analyst
Business Analysts help businesses improve their operations by identifying and solving business problems. They work with stakeholders to identify the root causes of business problems, and then they develop and implement solutions to address those problems. This course may be useful for Business Analysts as it provides a foundation in the evaluation of recommender systems. By understanding how to measure the effectiveness of these systems, Business Analysts can develop more effective solutions to business problems.
Data Architect
Data Architect
Data Architects design and manage the data architecture of organizations. They work with stakeholders to identify the data needs of the organization, and then they design and implement solutions to meet those needs. This course may be useful for Data Architects as it provides a foundation in the evaluation of recommender systems. By understanding how to measure the effectiveness of these systems, Data Architects can design and manage more effective data architectures.
For more career information including salaries, visit:
OpenCourser.com/course/ixsdph/recommender
Reading list
We've selected seven 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: Evaluation and Metrics.
This handbook provides a comprehensive overview of recommender systems, covering various techniques, evaluation methods, and applications. It serves as an excellent reference for understanding the foundational concepts and state-of-the-art advancements in the field.
Save
Focuses specifically on the evaluation of recommender systems, providing in-depth coverage of various evaluation metrics, experimental design, and statistical analysis techniques. It's an essential resource for researchers and practitioners seeking to understand and improve the performance of their recommender systems.
This textbook provides a comprehensive overview of recommender systems, covering both theoretical and practical aspects. It includes discussions on various recommendation algorithms, evaluation techniques, and real-world applications.
Provides a comprehensive overview of information retrieval, covering various techniques, algorithms, and applications. It serves as a valuable resource for understanding the foundational concepts and methods used in recommender systems.
Provides a comprehensive introduction to machine learning, covering various algorithms, techniques, and applications. It serves as a valuable reference for understanding the foundational concepts and methodologies used in recommender systems.
Save
Provides a comprehensive overview of data mining techniques, covering various algorithms, techniques, and applications. It serves as a valuable resource for understanding the foundational concepts and methods used in recommender systems.
Provides a comprehensive introduction to statistical methods used in data analysis, covering various techniques, algorithms, and applications. It serves as a valuable reference for understanding the statistical foundations and methodologies used in recommender systems.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/ixsdph/recommender
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Via
Coursera
Institution
University of Minnesota
Instructors
Michael D. Ekstrand
Joseph A Konstan
Language
English
Transcripts
Español
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Português
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中文
العربية
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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
- Recommender Systems Capstone
- Matrix Factorization and Advanced Techniques
- Recommender Systems: Evaluation and Metrics (viewing)
Good to know
Teaches a variety of metrics to evaluate recommender systems for different user goals and business goals
Develops skills in offline and online evaluation of recommender systems
Provides tools to compare different recommender system alternatives
Instructors are recognized for their work in the field of recommender systems
Provides both foundational and advanced knowledge in recommender system evaluation
May require some background knowledge in recommender systems or machine learning
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