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Building Practical Recommendation Engines – Part 1
Introduction to recommendation engines
The Course Overview (4:36)
Recommendation engine definition (4:12)
Types of recommender systems (5:19)
Evolution of recommender systems with technology (5:45)
Building your first recommendation engine
Loading and formatting data (6:04)
Calculating similarity between users (1:52)
Predicting the unknown ratings for users (7:43)
Recommendation engines explained
Nearest neighborhood-based recommendation engines (8:14)
Content-based recommender system (4:51)
Context-aware recommender system (3:14)
Hybrid recommender systems (2:48)
Model-based recommender systems (3:30)
Convolutional neural networks
Neighborhood-based techniques (10:37)
Mathematical model techniques (11:52)
Machine learning techniques (2:47)
Classification models (18:49)
Clustering techniques and dimensionality reduction. (7:56)
Vector space models (7:22)
Evaluation techniques (9:02)
Building Collaborative Filtering Recommendation Engines
Installing the recommenderlab package in RStudio (1:31)
Datasets available in the recommenderlab package (3:15)
Exploring the dataset andbuilding user-based collaborative filtering (17:35)
Building an item-based recommender model (10:40)
Collaborative filtering using Python (2:12)
Data exploration (5:37)
User-based collaborative filtering with the k-nearest neighbors (2:35)
Item-based recommendations (2:57)
Neighborhood-based techniques
Learn to know the simple mathematical calculation that is applied between two vectors
Calculating the Euclidean distance
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