Basic Recommender Systems
- Offered byCoursera
Basic Recommender Systems at Coursera Overview
Duration | 12 hours |
Total fee | Free |
Mode of learning | Online |
Difficulty level | Intermediate |
Official Website | Explore Free Course  |
Credential | Certificate |
Basic Recommender Systems at Coursera Highlights
- Shareable Certificate Earn a Certificate upon completion
- 100% online Start instantly and learn at your own schedule.
- Flexible deadlines Reset deadlines in accordance to your schedule.
- Intermediate Level Basic notions of linear algebra
- Approx. 12 hours to complete
- English Subtitles: English
Basic Recommender Systems at Coursera Course details
- 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. 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 to 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! This 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.
Basic Recommender Systems at Coursera Curriculum
BASIC CONCEPTS
Course overview and welcome by the instructor
Welcome by the instructor - module overview
Introduction to Recommender Systems
Taxonomy of Recommender Systems
Item-Content Matrix
User-Rating Matrix
Inferring Preferences
Recap by the instructor
Non-personalized Recommender Systems
Global Effects
Conclusions by the instructor
Course Syllabus
Credits & Acknowledgements
Module 1 - Graded Assessment
EVALUATION OF RECOMMENDER SYSTEMS
Welcome by the instructor - module overview
Quality of Recommender Systems
Quality Indicators
Online Evaluation Techniques
Offline Evaluation Techniques
Dataset Partitioning
Overfitting
Recap by the instructor
Error Metrics
Classification Metrics
Ranking Metrics
Conclusions by the instructor
Module 2 - Graded Assessment
CONTENT-BASED FILTERING
Welcome by the instructor - module overview
Content-based Filtering
Cosine Similarity
Matrix Notation
K-Nearest Neighbours
Recap by the instructor
Improving the ICM
TF-IDF
Conclusions by the instructor
Module 3 - Graded Assessment
COLLABORATIVE FILTERING
Welcome by the instructor - module overview
Collaborative Filtering
User-based CF
Recap by the instructor
Item-based CF
User-based vs. Item-based
Model-based vs. Memory-based
Recommendation as Association Rules
Conclusions by the instructor
Module 4 - Graded Assessment
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