Professional Courses
Industry-relevant training in Business, Technology, and Design to help professionals and graduates upskill for real-world careers.
Categories
Interactive Games
Fun, engaging games to boost memory, math fluency, typing speed, and English skills—perfect for learners of all ages.
Typing
Memory
Math
English Adventures
Knowledge
Enroll to start learning
You’ve not yet enrolled in this course. Please enroll for free to listen to audio lessons, classroom podcasts and take mock test.
Practice Questions
Test your understanding with targeted questions related to the topic.
Question 1
Easy
Define sparsity in the context of recommender systems.
💡 Hint: Think about the number of blanks in a user-item matrix.
Question 2
Easy
What is matrix factorization?
💡 Hint: Consider how you might simplify complex data by breaking it down.
Practice 4 more questions and get performance evaluation
Interactive Quizzes
Engage in quick quizzes to reinforce what you've learned and check your comprehension.
Question 1
What does sparsity refer to in recommender systems?
- Abundance of data
- Lack of user interactions
- Well-structured matrix
💡 Hint: Consider how many users engage with items.
Question 2
True or False: Matrix factorization can help uncover patterns in sparse data.
- True
- False
💡 Hint: Reflect on how breaking down data helps us see connections.
Solve 2 more questions and get performance evaluation
Challenge Problems
Push your limits with challenges.
Question 1
Consider a user-item matrix with 100 rows (users) and 50 columns (items). If there's an average of only 2 interactions per user, discuss the implications of sparsity.
💡 Hint: Reflect on how the limited interactions impact decision-making.
Question 2
Design a hybrid recommender system that addresses the sparsity problem using matrix factorization and collaborative filtering. Describe how these techniques would work together.
💡 Hint: Consider how each technique can fill in the gaps left by the other.
Challenge and get performance evaluation