Building Practical Recommendation Engines – Part 1

Video Description

Make Intelligent predictions with real-world projects

About This Video

  • A step-by-step guide to building recommendation engines in no time

  • Get to grips with the best tools available on the market to create efficient recommendation systems

  • This hands-on tutorial shows you how to implement different tools for recommendation engines, when to use a particular recommendation engine, and how

  • In Detail

    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.

    Table of Contents

    1. Chapter 1 : Introduction to recommendation engines
      1. The Course Overview 00:04:36
      2. Recommendation engine definition 00:04:13
      3. Types of recommender systems 00:05:19
      4. Evolution of recommender systems with technology 00:05:45
    2. Chapter 2 : Building your first recommendation engine
      1. Loading and formatting data 00:06:04
      2. Calculating similarity between users 00:01:52
      3. Predicting the unknown ratings for users 00:07:43
    3. Chapter 3 : Recommendation engines explained
      1. Nearest neighborhood-based recommendation engines 00:08:15
      2. Content-based recommender system 00:04:51
      3. Context-aware recommender system 00:03:14
      4. Hybrid recommender systems 00:02:48
      5. Model-based recommender systems 00:03:31
    4. Chapter 4 : Convolutional neural networks
      1. Neighborhood-based techniques 00:10:36
      2. Mathematical model techniques 00:11:50
      3. Machine learning techniques 00:02:47
      4. Classification models 00:18:47
      5. Clustering techniques and dimensionality reduction 00:07:57
      6. Vector space models 00:07:22
      7. Evaluation techniques 00:09:02
    5. Chapter 5 : Building Collaborative Filtering Recommendation Engines
      1. Installing the recommenderlab package in RStudio 00:01:31
      2. Datasets available in the recommenderlab package 00:03:14
      3. Exploring the dataset andbuilding user-based collaborative filtering 00:17:33
      4. Building an item-based recommender model 00:10:40
      5. Collaborative filtering using Python 00:02:11
      6. Data exploration 00:05:38
      7. User-based collaborative filtering with the k-nearest neighbors 00:02:36
      8. Item-based recommendations 00:02:56

    Product Information

    • Title: Building Practical Recommendation Engines – Part 1
    • Author(s): Suresh Kumar Gorakala
    • Release date: January 2017
    • Publisher(s): Packt Publishing
    • ISBN: 9781787287648