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.

EVALUATION OF RECOMMENDER SYSTEMS

In this second module, we'll learn how to define and measure the quality of a recommender system. We'll review different metrics that can be used to measure for this purpose. At the end of the module you'll be able to identify the correct evaluation activities required to measure the quality of a given recommender system, based on goals and needs.

CONTENT-BASED FILTERING

In this module we’ll analyse content-based recommender techniques. These algorithms recommend items similar to the ones a user liked in the past. We’ll review different similarity functions and you’ll then be able to choose the more suitable one for your system. The main input is the Item-Content Matrix (ICM) which describes all the attributes for each item. We’ll see how we can improve the quality of content-based techniques, by normalising and tuning the importance of each attribute in the ICM: you’ll be able to use some specific tuning strategies in order to obtain the best quality recommendations from your system. So, at the end of this module, you’ll know how to build a content-based recommender system, how to clean and normalize your input data.

COLLABORATIVE FILTERING

In this module we’ll study collaborative filtering techniques, which use the User Rating Matrix (URM) as the main input data, describing the interaction between users and items. We’ll learn how to build non-personalised recommender systems and how to normalise the URM, in order to provide better recommendations. At the end of the module you’ll be able to select the most appropriate similarity function and the most suitable way to compute similarity, overcoming issues related to explicit ratings.

Good to know

Know what's good

, what to watch for

, 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

Introduction to recommender systems

Learners say this introductory recommender systems course is well tailored and engaging. While there is useful content, there is a desire for more or improved hands on examples and assignments. Despite this, students say that they have learned a lot

Useful content.

"Well tailored content"

"There is a nice introduction to recommender systems field"

"I learned a lot from this quick course about Recommender systems."

Needs more hands on examples and/or assignments.

"Wish there were notebook examples and/or more hands on."

"Apart from the introductory (videos)...the others lessons are not even given by a real person. They are just slides red by a machine."

"The only assignments are things like: Take some time to think about this and write your thoughts in this textbox => USELESS"

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.

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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.

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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.

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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.

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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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