Books & Videos

Table of Contents

Chapter: Introduction to recommendation engines

The Course Overview

04m 36s

Recommendation engine definition

04m 12s

Types of recommender systems

05m 19s

Evolution of recommender systems with technology

05m 45s

Chapter: Building your first recommendation engine

Loading and formatting data

06m 4s

Calculating similarity between users

01m 52s

Predicting the unknown ratings for users

07m 43s

Chapter: Recommendation engines explained

Nearest neighborhood-based recommendation engines

08m 14s

Content-based recommender system

04m 51s

Context-aware recommender system

03m 14s

Hybrid recommender systems

02m 48s

Model-based recommender systems

03m 30s

Chapter: Convolutional neural networks

Neighborhood-based techniques

10m 35s

Mathematical model techniques

11m 49s

Machine learning techniques

02m 46s

Classification models

18m 47s

Clustering techniques and dimensionality reduction

07m 56s

Vector space models

07m 22s

Evaluation techniques

09m 2s

Chapter: Building Collaborative Filtering Recommendation Engines

Installing the recommenderlab package in RStudio

01m 31s

Datasets available in the recommenderlab package

03m 14s

Exploring the dataset andbuilding user-based collaborative filtering

17m 32s

Building an item-based recommender model

10m 40s

Collaborative filtering using Python

02m 11s

Data exploration

05m 37s

User-based collaborative filtering with the k-nearest neighbors

Item-based recommendations

02m 56s