Understanding Algorithms for Recommendation Systems
Recommendations help monetize user behavior data that businesses capture. This course is all about identifying user-product relationships from data using different recommendation algorithms.
What you'll learn
In addition to monetizing user behavior data, recommendation algorithms also help extract actionable recommendations from raw user ratings/purchases data. This course, Understanding Algorithms for Recommendation Systems, will cover the different types of Recommendation algorithms - Content-Based Filtering, Collaborative Filtering, and Association Rules Learning and when to use each of these types. You'll also learn about the specific algorithms such as the Nearest Neighbors model, Latent Factor Analysis and the Apriori Algorithm and implement them on real data sets. Finally, you'll learn about mining for rules that relate different products. By the end of this course, you'll be able to choose the recommendation algorithm that fits your problem and dataset, and apply it to find relevant recommendations.
Table of contents
- Understanding the Nearest Neighbors Model 7m
- Measuring Distance Between Users 6m
- Implementing the Nearest Neighbors Model 4m
- Setting up the Book Crossing Data Set 8m
- Creating the Rating Matrix 5m
- Computing the Distance Between Users 3m
- Finding Nearest Neighbors 4m
- Finding Top N Book Recommendations 6m
About the author
Swetha loves playing with data and crunching numbers to get cool insights. She is an alumnus of top schools like IIT Madras and IIM Ahmedabad. She was the first member of Flipkart’s elite Analytics team and was instrumental in scaling it to 100+ employees. Swetha has always had an entrepreneurial bent and a love for teaching. She now has the chance to do both as the co¬founder of Loonycorn, a content studio focused on providing high quality content for technical skill development. Loonycorn ... more