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Machine Learning
Recommender Systems & Dimensionality Reduction
Carlos Guestrin
and
Emily Fox
Carlos Guestrin
and
Emily Fox
Case Study: Recommending Products
How does Amazon recommend products you might be interested in purchasing? How does Netflix decide which movies or TV shows you might want to watch? What if you are a new user, should Netflix just recommend the most popular...
Read more
Case Study: Recommending Products
How does Amazon recommend products you might be interested in purchasing? How does Netflix decide which movies or TV shows you might want to watch? What if you are a new user, should Netflix just recommend the most popular...
Read more
Case Study: Recommending Products
How does Amazon recommend products you might be interested in purchasing? How does Netflix decide which movies or TV shows you might want to watch? What if you are a new user, should Netflix just recommend the most popular movies? Who might you form a new link with on Facebook or LinkedIn? These questions are endemic to most service-based industries, and underlie the notion of collaborative filtering and the recommender systems deployed to solve these problems. In this fourth case study, you will explore these ideas in the context of recommending products based on customer reviews.
In this course, you will explore dimensionality reduction techniques for modeling high-dimensional data. In the case of recommender systems, your data is represented as user-product relationships, with potentially millions of users and hundred of thousands of products. You will implement matrix factorization and latent factor models for the task of predicting new user-product relationships. You will also use side information about products and users to improve predictions.
Learning Outcomes: By the end of this course, you will be able to:
-Create a collaborative filtering system.
-Reduce dimensionality of data using SVD, PCA, and random projections.
-Perform matrix factorization using coordinate descent.
-Deploy latent factor models as a recommender system.
-Handle the cold start problem using side information.
-Examine a product recommendation application.
-Implement these techniques in Python.
Register for this course and see more details by visiting:
OpenCourser.com/course/c9imfq/Coursera
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Develops recommendations based on user reviews
Suitable for learners with technical background
Covers dimensionality reduction techniques
Builds on existing knowledge of matrix factorization
Involves implementing algorithms in Python
Focuses on collaborative filtering for product recommendations
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Reviews summary
Introductory recommender systems course
Machine Learning: Recommender Systems & Dimensionality Reduction offers an introduction to recommender systems and dimensionality reduction techniques. Based on a review of available reviews, the course is well-received in spite of issues accessing the course, but more context is needed for a definitive analysis.
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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 Machine Learning: Recommender Systems & Dimensionality Reduction with these
activities:
Review Calculus
Show steps
Review basic calculus concepts such as limits, derivatives, and integrals
Browse courses on
Calculus
Show steps
-
Review notes from previous calculus courses
-
Complete practice problems on differentiation and integration
Participate in Discussion Forums
Show steps
Engage in discussions with classmates to exchange ideas, ask questions, and clarify concepts
Browse courses on
Discussion
Show steps
-
Read through the discussion topics
-
Post questions or comments
-
Respond to posts from other students
Tutorial on using SVD for dimensionality reduction
Show steps
Enhance understanding of dimensionality reduction techniques, particularly SVD, in the context of recommender systems.
Show steps
-
Understand the concepts and principles of SVD
-
Learn how to apply SVD for dimensionality reduction in recommender systems
11 other activities
Expand to see all activities and additional details
Show all 14 activities
Follow Matrix Factorization Tutorials
Show steps
Complete tutorials on matrix factorization to gain a deeper understanding of the technique
Browse courses on
Matrix Factorization
Show steps
-
Find online tutorials or courses on matrix factorization
-
Follow the tutorials step by step
-
Implement matrix factorization algorithms in the provided code snippets
Diminishing the Curse of Dimensionality in Machine Learning
Show steps
Practice dimensionality reduction techniques on high-dimensional datasets to improve understanding of the curse of dimensionality.
Browse courses on
Dimensionality Reduction
Show steps
-
Generate high-dimensional synthetic dataset.
-
Apply dimensionality reduction techniques (e.g., PCA, SVD) to reduce dimensionality.
-
Evaluate performance of dimensionality reduction techniques using metrics like explained variance.
-
Visualize the reduced dimensionality using scatter plots or t-SNE.
Developing a Recommender System with Matrix Factorization
Show steps
Learn to build a basic recommender system using matrix factorization techniques, enhancing understanding of collaborative filtering.
Browse courses on
Matrix Factorization
Show steps
-
Import user-product interaction data and explore it.
-
Implement matrix factorization algorithm (e.g., SVD, ALS) to learn latent factors.
-
Predict user preferences for unseen products using the learned latent factors.
-
Evaluate the performance of the recommender system using metrics like RMSE or Precision@K.
Solve Coding Problems
Show steps
Practice solving coding problems on platforms like LeetCode to improve programming skills
Browse courses on
Coding
Show steps
-
Choose a coding problem
-
Design and implement an algorithm
-
Test and debug code
Practice collaborative filtering using matrix factorization
Show steps
Develop hands-on experience with matrix factorization techniques for collaborative filtering.
Show steps
-
Review matrix factorization techniques
-
Implement matrix factorization algorithm in Python
-
Evaluate performance of the algorithm on a sample dataset
Build a Recommender System
Show steps
Create a functional recommender system that can provide personalized recommendations based on user preferences
Browse courses on
Recommender Systems
Show steps
-
Gather data on users and products
-
Preprocess and transform the data
-
Train a matrix factorization model on the data
-
Evaluate the model's performance
-
Deploy the recommender system
Create a Recommender System Prototype
Show steps
Build a hands-on recommender system using matrix factorization techniques to reinforce your understanding of the concepts covered in the course.
Browse courses on
Recommender Systems
Show steps
-
Gather user-product interaction data.
-
Apply matrix factorization algorithms.
-
Evaluate the performance of your model.
Tutorial on deploying recommender systems
Show steps
Gain insights into best practices and strategies for deploying recommender systems in real-world scenarios.
Show steps
-
Explore different deployment architectures for recommender systems
-
Learn about scaling and performance optimization techniques
Designing a Personalized Recommendation Engine for a Streaming Service
Show steps
Develop a comprehensive recommendation engine that leverages various techniques to provide personalized recommendations, solidifying knowledge of recommender system design.
Show steps
-
Collect and analyze user behavior data (e.g., watch history, ratings).
-
Implement content-based filtering to recommend similar items based on item attributes.
-
Incorporate collaborative filtering to consider user preferences and social interactions.
-
Evaluate the recommender engine's performance and make improvements based on metrics like user engagement and satisfaction.
Practice handling the cold start problem with side information
Show steps
Build practical skills in leveraging side information to address the cold start problem in recommender systems.
Show steps
-
Identify relevant side information for products and users
-
Incorporate side information into the recommender system model
-
Evaluate the effectiveness of the side information in mitigating the cold start problem
Building a Movie Recommendation System with Natural Language Processing
Show steps
Develop a movie recommendation system that leverages natural language processing to analyze user reviews, deepening understanding of NLP applications in recommender systems.
Show steps
-
Collect and preprocess user reviews for movies.
-
Use NLP techniques (e.g., sentiment analysis, topic modeling) to extract insights from reviews.
-
Build a recommendation model that incorporates NLP-derived features along with traditional user-movie interactions.
-
Deploy the recommendation system and gather feedback to iteratively improve its performance.
Review Calculus
Show steps
Review basic calculus concepts such as limits, derivatives, and integrals
Browse courses on
Calculus
Show steps
Show steps
Show steps
- Review notes from previous calculus courses
- Complete practice problems on differentiation and integration
Participate in Discussion Forums
Show steps
Engage in discussions with classmates to exchange ideas, ask questions, and clarify concepts
Browse courses on
Discussion
Show steps
Show steps
Show steps
- Read through the discussion topics
- Post questions or comments
- Respond to posts from other students
Tutorial on using SVD for dimensionality reduction
Show steps
Enhance understanding of dimensionality reduction techniques, particularly SVD, in the context of recommender systems.
Show steps
Show steps
Show steps
- Understand the concepts and principles of SVD
- Learn how to apply SVD for dimensionality reduction in recommender systems
11 other activities
Expand to see all activities and additional details
Show all 14 activities
Follow Matrix Factorization Tutorials
Show steps
Complete tutorials on matrix factorization to gain a deeper understanding of the technique
Browse courses on
Matrix Factorization
Show steps
Show steps
Show steps
- Find online tutorials or courses on matrix factorization
- Follow the tutorials step by step
- Implement matrix factorization algorithms in the provided code snippets
Diminishing the Curse of Dimensionality in Machine Learning
Show steps
Practice dimensionality reduction techniques on high-dimensional datasets to improve understanding of the curse of dimensionality.
Browse courses on
Dimensionality Reduction
Show steps
Show steps
Show steps
- Generate high-dimensional synthetic dataset.
- Apply dimensionality reduction techniques (e.g., PCA, SVD) to reduce dimensionality.
- Evaluate performance of dimensionality reduction techniques using metrics like explained variance.
- Visualize the reduced dimensionality using scatter plots or t-SNE.
Developing a Recommender System with Matrix Factorization
Show steps
Learn to build a basic recommender system using matrix factorization techniques, enhancing understanding of collaborative filtering.
Browse courses on
Matrix Factorization
Show steps
Show steps
Show steps
- Import user-product interaction data and explore it.
- Implement matrix factorization algorithm (e.g., SVD, ALS) to learn latent factors.
- Predict user preferences for unseen products using the learned latent factors.
- Evaluate the performance of the recommender system using metrics like RMSE or Precision@K.
Solve Coding Problems
Show steps
Practice solving coding problems on platforms like LeetCode to improve programming skills
Browse courses on
Coding
Show steps
Show steps
Show steps
- Choose a coding problem
- Design and implement an algorithm
- Test and debug code
Practice collaborative filtering using matrix factorization
Show steps
Develop hands-on experience with matrix factorization techniques for collaborative filtering.
Show steps
Show steps
Show steps
- Review matrix factorization techniques
- Implement matrix factorization algorithm in Python
- Evaluate performance of the algorithm on a sample dataset
Build a Recommender System
Show steps
Create a functional recommender system that can provide personalized recommendations based on user preferences
Browse courses on
Recommender Systems
Show steps
Show steps
Show steps
- Gather data on users and products
- Preprocess and transform the data
- Train a matrix factorization model on the data
- Evaluate the model's performance
- Deploy the recommender system
Create a Recommender System Prototype
Show steps
Build a hands-on recommender system using matrix factorization techniques to reinforce your understanding of the concepts covered in the course.
Browse courses on
Recommender Systems
Show steps
Show steps
Show steps
- Gather user-product interaction data.
- Apply matrix factorization algorithms.
- Evaluate the performance of your model.
Tutorial on deploying recommender systems
Show steps
Gain insights into best practices and strategies for deploying recommender systems in real-world scenarios.
Show steps
Show steps
Show steps
- Explore different deployment architectures for recommender systems
- Learn about scaling and performance optimization techniques
Designing a Personalized Recommendation Engine for a Streaming Service
Show steps
Develop a comprehensive recommendation engine that leverages various techniques to provide personalized recommendations, solidifying knowledge of recommender system design.
Show steps
Show steps
Show steps
- Collect and analyze user behavior data (e.g., watch history, ratings).
- Implement content-based filtering to recommend similar items based on item attributes.
- Incorporate collaborative filtering to consider user preferences and social interactions.
- Evaluate the recommender engine's performance and make improvements based on metrics like user engagement and satisfaction.
Practice handling the cold start problem with side information
Show steps
Build practical skills in leveraging side information to address the cold start problem in recommender systems.
Show steps
Show steps
Show steps
- Identify relevant side information for products and users
- Incorporate side information into the recommender system model
- Evaluate the effectiveness of the side information in mitigating the cold start problem
Building a Movie Recommendation System with Natural Language Processing
Show steps
Develop a movie recommendation system that leverages natural language processing to analyze user reviews, deepening understanding of NLP applications in recommender systems.
Show steps
Show steps
Show steps
- Collect and preprocess user reviews for movies.
- Use NLP techniques (e.g., sentiment analysis, topic modeling) to extract insights from reviews.
- Build a recommendation model that incorporates NLP-derived features along with traditional user-movie interactions.
- Deploy the recommendation system and gather feedback to iteratively improve its performance.
Career center
Learners who complete Machine Learning: Recommender Systems & Dimensionality Reduction will develop knowledge and skills
that may be useful to these careers:
Data Scientist
Data Scientist
A Data Scientist is responsible for using data to solve business problems. They collect, clean, and analyze data to extract meaningful insights. This course will help you develop the skills needed to become a Data Scientist, including data mining, machine learning, and statistical modeling. You will also learn how to communicate your findings to business stakeholders.
Machine Learning Engineer
Machine Learning Engineer
A Machine Learning Engineer is responsible for designing and implementing machine learning models. They work closely with data scientists to identify business problems that can be solved with machine learning, and then develop and deploy models that solve these problems. This course will help you develop the skills needed to become a Machine Learning Engineer, including machine learning algorithms, model evaluation, and deployment.
Software Engineer
Software Engineer
A Software Engineer is responsible for designing, developing, and maintaining software applications. They work with users to understand their needs, and then design and implement software solutions that meet those needs. This course will help you develop the skills needed to become a Software Engineer, including software design, development, and testing.
Product Manager
Product Manager
A Product Manager is responsible for managing the development and launch of new products. They work with engineers, designers, and marketers to bring products to market that meet the needs of customers. This course will help you develop the skills needed to become a Product Manager, including product strategy, development, and marketing.
Business Analyst
Business Analyst
A Business Analyst is responsible for analyzing business processes and identifying ways to improve them. They work with stakeholders to understand their needs, and then develop and implement solutions that meet those needs. This course will help you develop the skills needed to become a Business Analyst, including business process analysis, modeling, and improvement.
Marketing Analyst
Marketing Analyst
A Marketing Analyst is responsible for analyzing marketing data to identify trends and opportunities. They work with marketing managers to develop and implement marketing campaigns that reach the target audience and achieve business goals. This course will help you develop the skills needed to become a Marketing Analyst, including marketing research, data analysis, and campaign management.
Data Analyst
Data Analyst
A Data Analyst is responsible for collecting, cleaning, and analyzing data. They work with stakeholders to understand their needs, and then develop and implement solutions that meet those needs. This course will help you develop the skills needed to become a Data Analyst, including data mining, machine learning, and statistical modeling.
Quantitative Analyst
Quantitative Analyst
A Quantitative Analyst is responsible for using mathematical and statistical models to analyze financial data. They work with portfolio managers to develop and implement investment strategies. This course will help you develop the skills needed to become a Quantitative Analyst, including financial modeling, statistical analysis, and risk management.
Operations Research Analyst
Operations Research Analyst
An Operations Research Analyst is responsible for using mathematical and statistical models to solve business problems. They work with managers to identify problems and develop and implement solutions that improve efficiency and productivity. This course will help you develop the skills needed to become an Operations Research Analyst, including mathematical modeling, optimization, and simulation.
Statistician
Statistician
A Statistician is responsible for collecting, analyzing, and interpreting data. They work with researchers to design studies, collect data, and analyze the results. This course will help you develop the skills needed to become a Statistician, including statistical modeling, data analysis, and interpretation.
Actuary
Actuary
An Actuary is responsible for using mathematical and statistical models to assess risk. They work with insurance companies to develop and implement products that protect against financial loss. This course will help you develop the skills needed to become an Actuary, including risk modeling, financial analysis, and insurance.
Financial Analyst
Financial Analyst
A Financial Analyst is responsible for analyzing financial data to make investment recommendations. They work with clients to identify their financial goals and develop and implement investment strategies that achieve those goals. This course will help you develop the skills needed to become a Financial Analyst, including financial modeling, investment analysis, and portfolio management.
Economist
Economist
An Economist is responsible for studying the production, distribution, and consumption of goods and services. They work with governments and businesses to develop and implement policies that improve economic growth and stability. This course will help you develop the skills needed to become an Economist, including economic modeling, data analysis, and policy analysis.
Market Researcher
Market Researcher
A Market Researcher is responsible for collecting and analyzing data about markets and consumers. They work with businesses to develop and implement marketing strategies that reach the target audience and achieve business goals. This course will help you develop the skills needed to become a Market Researcher, including market research, data analysis, and marketing.
Data Entry Clerk
Data Entry Clerk
A Data Entry Clerk is responsible for entering data into a computer system. They work with businesses to ensure that data is accurate and up-to-date. This course may be useful for developing the skills needed to become a Data Entry Clerk, including data entry and data management.
For more career information including salaries, visit:
OpenCourser.com/course/c9imfq/Coursera
Reading list
We've selected 13 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
Machine Learning: Recommender Systems & Dimensionality Reduction.
Provides a comprehensive overview of recommender systems, including both theoretical foundations and practical algorithms. It covers a wide range of topics, from collaborative filtering to content-based filtering to hybrid approaches.
Provides a comprehensive overview of recommender systems, including both theoretical foundations and practical algorithms. It covers a wide range of topics, from collaborative filtering to content-based filtering to hybrid approaches.
Provides a comprehensive overview of latent factor models for recommender systems. It covers both theoretical foundations and practical algorithms, and includes case studies and examples from real-world systems.
Provides a comprehensive overview of dimensionality reduction techniques, which are used to reduce the number of features in a dataset without losing important information. It covers both the theoretical foundations and practical applications of these techniques, making it a valuable resource for researchers and practitioners alike.
Save
Provides a comprehensive overview of machine learning. It covers various algorithms, data preparation techniques, and evaluation methods.
Save
Provides a comprehensive overview of practical recommender systems. It covers both the theoretical foundations and practical applications of these systems, making it a valuable resource for researchers and practitioners alike.
Provides a comprehensive overview of recommender systems. It covers both the theoretical foundations and practical applications of these systems, making it a valuable resource for researchers and practitioners alike.
Provides a comprehensive overview of dimensionality reduction techniques for machine learning. It covers both theoretical foundations and practical algorithms, and includes case studies and examples from real-world systems. While it is not specific to recommender systems, it provides a strong foundation for understanding the techniques used in this course.
Save
Provides a comprehensive overview of deep learning. It covers various algorithms, data preparation techniques, and evaluation methods.
Provides a comprehensive overview of natural language processing using Python. It covers various algorithms, data preparation techniques, and evaluation methods.
Provides a comprehensive overview of information retrieval. It covers various algorithms, data preparation techniques, and evaluation methods.
Provides a practical guide to building recommender systems using machine learning. It covers various algorithms, data preparation techniques, and evaluation methods.
Provides a comprehensive overview of principal component analysis (PCA). It covers the theory, algorithms, and applications of PCA.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/c9imfq/Coursera
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Via
Coursera
Institution
University of Washington
Instructors
Carlos Guestrin
Emily Fox
Good to know
Develops recommendations based on user reviews
Suitable for learners with technical background
Covers dimensionality reduction techniques
Builds on existing knowledge of matrix factorization
Involves implementing algorithms in Python
Focuses on collaborative filtering for product recommendations
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