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Learning Path: Build Your Own Recommendation Engines

Video Introducing this tutorial

Chapter 1 : Building Practical Recommendation Engines – Part 1
The Course Overview 00:04:36
Recommendation engine definition 00:04:13
Types of recommender systems 00:05:19
Evolution of recommender systems with technology 00:05:45
Loading and formatting data 00:06:04
Calculating similarity between users 00:01:52
Predicting the unknown ratings for users 00:07:43
Nearest neighborhood-based recommendation engines 00:08:15
Content-based recommender system 00:04:51
Context-aware recommender system 00:03:14
Hybrid recommender systems 00:02:48
Model-based recommender systems 00:03:31
Neighborhood-based techniques 00:10:36
Mathematical model techniques 00:11:50
Machine learning techniques 00:02:47
Classification models 00:18:47
Clustering techniques and dimensionality reduction 00:07:57
Vector space models 00:07:22
Evaluation techniques 00:09:02
Installing the recommenderlab package in RStudio 00:01:31
Datasets available in the recommenderlab package 00:03:14
Exploring the dataset andbuilding user-based collaborative filtering 00:17:33
Building an item-based recommender model 00:10:40
Collaborative filtering using Python 00:02:11
Data exploration 00:05:38
User-based collaborative filtering with the k-nearest neighbors 00:02:36
Item-based recommendations 00:02:56

Chapter 2 : Building Practical Recommendation Engines – Part 2
The Course Overview 00:03:03
Personalized and Content-Based Recommender System 00:10:22
Content-Based Recommendation Using Python 00:08:16
Context-Aware Recommender Systems 00:02:23
Creating Context Profile 00:04:12
About Spark 2.0 00:03:44
Spark Core 00:03:33
Setting Up Spark 00:05:12
Collaborative Filtering Using Alternating Least Square 00:03:34
Model Based Recommender System Using pyspark 00:02:19
The Recommendation Engine Approach 00:09:24
Model Evaluation and Selection with Hyper Parameter Tuning 00:10:26
Discerning Different Graph Databases 00:07:08
Neo4j 00:03:23
Building Your First Graph 00:04:01
Neo4j Windows Installation 00:01:07
Installing Neo4j on the Linux Platform 00:01:48
Building Recommendation Engines 00:03:05
Generating Recommendations Using Neo4j 00:01:52
Collaborative filtering Using the Euclidean Distance 00:03:38
Collaborative Filtering Using Cosine Similarity 00:02:20
Setting up Mahout with General Introduction 00:04:21
Core Building Blocks of Mahout 00:10:16
Item-Based Collaborative Filtering 00:02:50
Evaluating Collaborative Filtering with User-Item Based Recommenders 00:03:41
SVD Recommenders 00:01:55
Future and Phases of Recommendation Engines 00:07:52
Using Cases to Look Out for 00:01:58
Popular Methodologies 00:04:47

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