Building Practical Recommendation Engines – Part 1

Building Practical Recommendation Engines – Part 1

English | MP4 | AVC 1280×720 | AAC 44KHz 2ch | 2h 52m Hours | 649 MB

Make Intelligent predictions with real-world projects

A recommendation engine (sometimes referred to as a recommender system) is a tool that lets algorithm developers predict what a user may or may not like among a list of given items. Recommender systems have become extremely common in recent years, and are applied in a variety of applications. The most popular ones are movies, music, news, books, research articles, search queries, social tags, and products in general.

This video starts with an introduction to recommendation systems and its applications. You will then start building recommendation engines straight away from the very basics. As you move along, you will learn to build recommender systems with popular frameworks such as R, Python, and more. You will get an insight into the pros and cons of different recommendation engines and when to use which recommendation.

With the help of this course, you will quickly get up and running with Recommender systems. You will create recommendation engines of varying complexities, ranging from a simple recommendation engine to real-time recommendation engines.

What You Will Learn

  • Discover the tools needed to build recommendation engines
  • Dive into the various techniques of recommender systems such as collaborative, content-based, and cross-recommendations
  • Create efficient decision-making systems that will ease your work
Table of Contents

Introduction to recommendation engines
01 The Course Overview
02 Recommendation engine definition
03 Types of recommender systems
04 Evolution of recommender systems with technology

Building your first recommendation engine
05 Loading and formatting data
06 Calculating similarity between users
07 Predicting the unknown ratings for users

Recommendation engines explained
08 Nearest neighborhood-based recommendation engines
09 Content-based recommender system
10 Context-aware recommender system
11 Hybrid recommender systems
12 Model-based recommender systems

Convolutional neural networks
13 Neighborhood-based techniques
14 Mathematical model techniques
15 Machine learning techniques
16 Classification models
17 Clustering techniques and dimensionality reduction
18 Vector space models
19 Evaluation techniques

Building Collaborative Filtering Recommendation Engines
20 Installing the recommenderlab package in RStudio
21 Datasets available in the recommenderlab package
22 Exploring the dataset andbuilding user-based collaborative filtering
23 Building an item-based recommender model
24 Collaborative filtering using Python
25 Data exploration
26 User-based collaborative filtering with the k-nearest neighbors
27 Item-based recommendations