Basic Recommender Systems from Coursera

The Basic Recommender Systems course introduces you to the leading approaches in recommender systems. The techniques described touch both collaborative and content-based approaches and include the most important algorithms used to provide recommendations. You'll learn how they work, how to use and how to evaluate them, pointing out benefits and limits of different recommender system alternatives.

After completing this course, you'll be able to describe the requirements and objectives of recommender systems based on different application domains. You'll know how to distinguish recommender systems according to their input data, their internal working mechanisms, and their goals. You’ll have the tools to measure the quality of a recommender system and incrementally improve it with the design of new algorithms. You'll learn as well how to design recommender systems tailored for new application domains, also considering surrounding social and ethical issues such as identity, privacy, and manipulation.

Providing affordable, personalised and high-quality recommendations is always a challenge! The course also leverages two important EIT Overarching Learning Outcomes (OLOs), related to creativity and innovation skills. In trying to design a new recommender system you need to think beyond boundaries and try to figure out how you can improve the quality of the predictions. You should also be able to use knowledge, ideas and technology to create new or significantly improved recommendation tools to support choice-making processes and strategies in different and innovative scenarios, for a better quality of life.

What's inside

Syllabus

BASIC CONCEPTS

In this first module, we'll review the basic concepts for recommender systems in order to classify and analyse different families of algorithms, related to specific set of input data. At the end, you’ll be able to choose the most suitable type of algorithm based on the data available, your needs and goals. Conversely, you'll know how to select the input data based on the algorithm you want to use.

Traffic lights

Read about what's good

what should give you pause

and possible dealbreakers

Teaches the industry standard approaches for generating recommendations

Provides thorough guidance on how to design and implement recommender systems

Covers the leading approaches in recommender systems, including collaborative and content-based approaches

Helpers students understand how to use and evaluate recommender systems, pointing out benefits and limits

Offers a strong foundation for students who want to build and improve recommender systems

Provides tools to measure the quality of recommender systems and incrementally improve them

Reviews summary

Foundational theory for recommender systems beginners

According to learners, this course offers a solid foundation in recommender systems, particularly excelling in its clear theoretical explanations of collaborative and content-based filtering. Many find it an excellent starting point for beginners, appreciating its logical structure and the valuable module on evaluation metrics. However, a notable portion of students desired more hands-on coding and practical application, leading to some finding the course too basic if they had prior machine learning experience. Some also noted that certain concepts felt a bit outdated. Overall, it's a strong theoretical introduction.

Includes valuable modules on evaluation metrics and ethical considerations.

"I especially appreciated the modules on evaluation metrics, which are often overlooked."

"The ethical considerations were an interesting addition."

"The ethical considerations were a unique and important aspect; it's good to see this covered."

Perfectly suited for those new to recommender systems.

"Excellent course for beginners! ... it sets you up well."

"Good starting point for someone new to the field."

"For someone new to the field, this is excellent."

"I found this to be a great entry point for understanding recommender systems from the ground up."

Provides highly clear and digestible theoretical explanations.

"This course provided a really solid foundation in recommender systems. The explanations of collaborative and content-based filtering were incredibly clear."

"Excellent course for beginners! The structure is logical, and the concepts are presented in an easy-to-digest manner."

"Super clear and concise explanations. The course lays out the foundational concepts perfectly."

"I gained a solid understanding of how different recommender system algorithms work theoretically."

Some aspects of the course content may feel slightly outdated.

"My main critique is that it could benefit from updated examples or more modern libraries, as some concepts felt a little dated."

"The content is okay, but it feels a bit dated. I'm looking for more modern approaches and libraries."

"I wished for more contemporary examples of algorithms and frameworks currently used in the industry."

May be too basic for those with prior machine learning experience.

"Found it to be a bit too basic for my needs. As someone with some prior ML experience, I was looking for more depth in implementation."

"Felt like it was aimed at total beginners without any programming background, which wasn't clear."

"As an intermediate learner, I felt the content covered was mostly a review of what I already knew."

Lacks sufficient hands-on coding and implementation exercises.

"I wished for more hands-on coding... the practical application was a bit lacking."

"The lack of robust programming examples was disappointing. I needed to seek external resources for practical application."

"Disappointing. The course barely touched upon practical coding. It's all theory with very little application."

"I would have liked more substantial projects to apply what I learned in a real-world setting."

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 Basic Recommender Systems with these activities:

Review machine learning algorithms

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Take a refresher course or review materials on machine learning algorithms to strengthen your foundation in this area, which is crucial for understanding recommender systems.

Browse courses on Machine Learning Algorithms

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Identify the key concepts and algorithms in supervised learning
Understand the different types of unsupervised learning algorithms
Practice implementing some basic machine learning algorithms

Create a collection of course resources

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Organize and compile your course notes, assignments, quizzes, and exams to improve retention and understanding of the course material.

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Gather your course notes, assignments, quizzes, and exams
Organize the materials by topic or module
Consider creating summaries or outlines of key concepts

Mentor or tutor other students in this course

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Help solidify your own understanding of concepts by explaining them to other students, answering their questions, and providing guidance.

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Identify opportunities to help other students in the course, such as through discussion forums or study groups
Prepare and organize your knowledge and materials
Provide clear and concise explanations and guidance to other students

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Career center

Learners who complete Basic Recommender Systems will develop knowledge and skills that may be useful to these careers:

Data Scientist

Data Scientists use data to find solutions to problems, making them similar to Recommender Systems, which also use data to provide solutions. This course will teach you to implement algorithms for sorting data, measure quality of data, and how to improve data. A good foundation in these skills can help make you a successful Data Scientist.

See salaries and explore the career path for Data Scientist

Database Administrator

Database Administrators work to ensure the smooth operation of databases. With the help of recommender systems, they can analyze data to determine which processes are most impactful, making their work more efficient. This course will teach you the importance of data cleansing and tuning, which will directly translate to success as a Database Administrator.

See salaries and explore the career path for Database Administrator

Software Developer

Software Developers translate coding language into functionality, much like Recommender Systems use mathematical formulas to sort data. This course will teach you the importance of choosing the correct input data and information to build better algorithms, a skill which can be useful to Software Developers at any level.

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Product Manager

Product Managers use data to improve products, which Recommender Systems can aid in by sorting relevant data. This course will teach you about different types of filtering that can help translate data into easy to use formats, which as a Product Manager, can help to improve product functionality.

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IT Manager

IT Managers use data to improve processes and make data-driven decisions, making them similar to Recommender Systems, which also use data to find solutions. This course will teach you about different types of data and how to evaluate which type is most relevant to your needs, skills necessary to be successful as an IT Manager.

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Business Analyst

Business Analysts use data to make recommendations to businesses and companies, much like Recommender Systems, which also use data to provide solutions. This course will teach you to implement algorithms for sorting data, measure quality of data, and how to improve data. A good foundation in these skills can help make you a successful Business Analyst.

See salaries and explore the career path for Business Analyst

Data Analyst

Data Analysts work with data to find insights and help organizations make better decisions. Recommender systems can help by sorting relevant data to identify patterns and trends. This course will teach you to implement algorithms for sorting data, measure quality of data, and how to improve data. These skills will help you succeed as a Data Analyst.

See salaries and explore the career path for Data Analyst

Marketing Manager

Marketing Managers use data to plan, develop, and execute marketing campaigns. Recommender systems can help identify target audiences and personalize marketing messages. This course will teach you about different types of filtering that can help translate data into easy to use formats, which as a Marketing Manager, can help to improve campaign results.

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Sales Manager

Sales Managers use data to track sales performance and identify opportunities for growth. Recommender systems can help identify potential customers and personalize sales pitches. This course will teach you about different types of filtering that can help translate data into easy to use formats, which as a Sales Manager, can help to improve sales outcomes.

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Customer Relationship Manager

Customer Relationship Managers use data to build relationships with customers and improve customer satisfaction. Recommender systems can help identify customer needs and personalize interactions. This course will teach you how to evaluate the quality of data and how to improve data. These skills will help you succeed as a Customer Relationship Manager.

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Operations Manager

Operations Managers use data to improve efficiency and productivity. Recommender systems can help identify bottlenecks and optimize processes. This course will teach you to implement algorithms for sorting data and measure quality of data. These skills will help you succeed as an Operations Manager.

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Financial Analyst

Financial Analysts use data to make investment recommendations and evaluate financial performance. Recommender systems can help identify investment opportunities and personalize financial advice. This course will teach you about different types of filtering that can help translate data into easy to use formats, which as a Financial Analyst, can help to improve investment outcomes.

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Actuary

Actuaries use data to assess risk and make insurance recommendations. Recommender systems can help identify risk factors and personalize insurance policies. This course will teach you about different types of filtering that can help translate data into easy to use formats, which as an Actuary, can help to improve insurance outcomes.

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Auditor

Auditors use data to assess financial statements and ensure compliance with regulations. Recommender systems can help identify potential fraud and errors. This course will teach you about different types of filtering that can help translate data into easy to use formats, which as an Auditor, can help to improve audit outcomes.

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Consultant

Consultants use data to help organizations improve their performance. Recommender systems can help identify areas for improvement and develop solutions. This course will teach you to implement algorithms for sorting data, measure quality of data, and how to improve data. These skills will help you succeed as a Consultant.

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