Introduction to Recommender Systems: Non-Personalized and Content-Based
- 4.5
-
![]()
![]()
![]()
![]()
![]()
Approx. 23 hours to complete
Course Summary
In this course, you'll learn the basics of Recommender Systems and how they can be used to personalize online experiences. You'll explore the different types of Recommender Systems, such as Content-Based and Collaborative Filtering, and get hands-on experience building and evaluating them using Python.Key Learning Points
- Understand the different types of Recommender Systems
- Learn how to build and evaluate Recommender Systems using Python
- Explore real-world applications of Recommender Systems
Job Positions & Salaries of people who have taken this course might have
- USA: $113,000
- India: ₹1,456,000
- Spain: €39,000
- USA: $113,000
- India: ₹1,456,000
- Spain: €39,000
- USA: $117,000
- India: ₹1,600,000
- Spain: €42,000
- USA: $113,000
- India: ₹1,456,000
- Spain: €39,000
- USA: $117,000
- India: ₹1,600,000
- Spain: €42,000
- USA: $131,000
- India: ₹1,760,000
- Spain: €47,000
Related Topics for further study
Learning Outcomes
- Understand the basics of Recommender Systems
- Learn how to implement different types of Recommender Systems
- Be able to evaluate Recommender Systems and improve their performance
Prerequisites or good to have knowledge before taking this course
- Basic knowledge of Python Programming
- Familiarity with Data Science concepts
Course Difficulty Level
IntermediateCourse Format
- Online
- Self-paced
- Video lectures
- Hands-on projects
Similar Courses
- Applied Data Science: Machine Learning
- Reinforcement Learning
Related Education Paths
Related Books
Description
This course, which is designed to serve as the first course in the Recommender Systems specialization, introduces the concept of recommender systems, reviews several examples in detail, and leads you through non-personalized recommendation using summary statistics and product associations, basic stereotype-based or demographic recommendations, and content-based filtering recommendations.
Outline
- Preface
- Intro to Recommender Systems
- Intro to Course and Specialization
- Notes on Course Design and Relationship to Prior Courses
- Introducing Recommender Systems
- Movielens Tour
- Preferences and Ratings
- Predictions and Recommendations
- Taxonomy of Recommenders I
- Taxonomy of Recommenders II
- Tour of Amazon.com
- Recommender Systems: Past, Present and Future
- Introducing the Honors Track
- Honors: Setting up the development environment
- About the Honors Track
- Downloads and Resources
- Closing Quiz: Introducing Recommender Systems
- Honors Track Pre-Quiz
- Non-Personalized and Stereotype-Based Recommenders
- Non-Personalized and Stereotype-Based Recommenders
- Summary Statistics I
- Summary Statistics II
- Demographics and Related Approaches
- Product Association Recommenders
- Assignment #1 Intro Video
- Assignment Intro: Programming Non-Personalized Recommenders
- External Readings on Ranking and Scoring
- Assignment 1 Instructions: Non-Personalized and Stereotype-Based Recommenders
- Assignment Intro: Programming Non-Personalized Recommenders
- LensKit Resources
- Rating Data Information
- Assignment #1: Response #1: Top Movies by Mean Rating
- Assignment #1: Response #2: Top Movies by Count
- Assignment #1: Response #3: Top Movies by Percent Liking
- Assignment #1: Response #4: Association with Toy Story
- Assignment #1: Response #5: Correlation with Toy Story
- Assignment #1: Response #6: Male-Female Differences in Average Rating
- Assignment #1: Response #7: Male-Female differences in Liking
- Non-Personalized Recommenders
- Content-Based Filtering -- Part I
- Introduction to Content-Based Recommenders
- TFIDF and Content Filtering
- Content-Based Filtering: Deeper Dive
- Entree Style Recommenders -- Robin Burke Interview
- Case-Based Reasoning -- Interview with Barry Smyth
- Dialog-Based Recommenders -- Interview with Pearl Pu
- Search, Recommendation, and Target Audiences -- Interview with Sole Pera
- Beyond TFIDF -- Interview with Pasquale Lops
- Content-Based Filtering -- Part II
- Assignment #2 Introduction: Content-Based Filtering in a Spreadsheet
- Honors: Intro to programming assignment
- Content-Based Recommenders Spreadsheet Assignment (aka Assignment #2)
- Tools for Content-Based Filtering
- CBF Programming Intro
- Assignment #2 Answer Form
- Content-Based Filtering
- Course Wrap-up
- Unified Mathematical Model
- Psychology of Preference & Rating -- Interview with Martijn Willemsen
- Related Readings
Summary of User Reviews
This course on recommender systems is highly recommended by its users. It covers the basics of recommendation techniques and algorithms. One of the key aspects that many users appreciate is the practical approach to learning.Key Aspect Users Liked About This Course
Practical approach to learningPros from User Reviews
- Clear and concise explanations of concepts
- Good mixture of theoretical and practical aspects
- Expert instructors with relevant industry experience
- Hands-on programming assignments to apply what you've learned
Cons from User Reviews
- Some users found the course difficult to follow without prior knowledge of linear algebra
- Limited coverage of advanced topics in recommendation systems
- Lack of interaction with instructors or other students
Joseph A Konstan
4.5 Raiting
Machine Learning with Python
- 4.7
-
![]()
![]()
![]()
![]()
![]()
This course dives into the basics of machine learning using an approachable, and well-known programming language, Python. In this course, we will be reviewing two main components:...
Big Data Applications: Machine Learning at Scale
- 3.8
-
![]()
![]()
![]()
![]()
![]()
Machine learning is transforming the world around us. To become successful, you’d better know what kinds of problems can be solved with machine learning, and how they can be solved....
Machine Learning
- 4.9
-
![]()
![]()
![]()
![]()
![]()
Machine learning is the science of getting computers to act without being explicitly programmed. Many researchers also think it is the best way to make progress towards human-level AI....
Basic Recommender Systems
- 3.9
-
![]()
![]()
![]()
![]()
![]()
This 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....
