Understanding Algorithms for Recommendation Systems – Tutorial Understanding Recommended System Algorithms
Recommendations help monetize user behavior data that businesses capture. This course is all about identifying user-product relationships from data using different algorithms.
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 various types of Recommendation algorithms – Content-Based Filtering, Collaborative Filtering, and Association Rules. Learning and when to use each of these types. You will also learn about specific algorithms such as the Nearest Neighbor 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 data set and apply it to find relevant recommendations.
Of Contents Table:
– Course Overview
– Understanding Tasks Performed by Recommendation Systems
– Recommending Products Based on the Nearest Neighbors Model
– Recommending Products Based on the Latent Factors Model
– Data Mining for Rules Underlying User Behavior
Manufacturer: Plvralsayt / Pluralsight
language of instruction: English
Lecturer: Swetha Kolalapudi
Training time: 2 hours + 13 minutes
File size: 187 MB