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Building Practical Recommendation Engines – Part 1
Introduction to recommendation engines
The Course Overview
Recommendation engine definition
Types of recommender systems
Evolution of recommender systems with technology
Building your first recommendation engine
Loading and formatting data
Calculating similarity between users
Predicting the unknown ratings for users
Recommendation engines explained
Nearest neighborhood-based recommendation engines
Content-based recommender system
Context-aware recommender system
Hybrid recommender systems
Model-based recommender systems
Convolutional neural networks
Neighborhood-based techniques
Mathematical model techniques
Machine learning techniques
Classification models
Clustering techniques and dimensionality reduction.
Vector space models
Evaluation techniques
Building Collaborative Filtering Recommendation Engines
Installing the recommenderlab package in RStudio
Datasets available in the recommenderlab package
Exploring the dataset andbuilding user-based collaborative filtering
Building an item-based recommender model
Collaborative filtering using Python
Data exploration
User-based collaborative filtering with the k-nearest neighbors
Item-based recommendations
Neighborhood-based techniques
Learn to know the simple mathematical calculation that is applied between two vectors
Calculating the Euclidean distance
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