Building Practical Recommendation Engines – Part 1
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Building Practical Recommendation Engines – Part 1

Make Intelligent predictions with real-world projects
0.0 (0 ratings)
Instead of using a simple lifetime average, Udemy calculates a course's star rating by considering a number of different factors such as the number of ratings, the age of ratings, and the likelihood of fraudulent ratings.
7 students enrolled
Created by Packt Publishing
Last updated 2/2017
Current price: $10 Original price: $125 Discount: 92% off
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  • 3 hours on-demand video
  • Full lifetime access
  • Access on mobile and TV
  • Certificate of Completion
What Will I 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
View Curriculum
  • To work along this course you'll need to have complex predictive decision-making systems, recommendation engines using R. Familiarity with Python, Spark, Neo4j, and Hadoop.
  • The software requirements of the course are:
  • R studio Version 0.99.489
  • R version 3.2.2
  • Anaconda 4.2 for Python 3.5
  • WINDOWS 7+/Centos 6

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.

About The Author

Suresh Kumar Gorakala is a Data scientist focused on Artificial Intelligence. He has professional experience close to 10 years, having worked with various global clients across multiple domains and helped them in solving their business problems using Advanced Big Data Analytics. He has extensively worked on Recommendation Engines, Natural language Processing, Advanced Machine Learning, Graph Databases. He previously co-authored Building a Recommendation System with R for Packt Publishing. He is a passionate traveler and is a photographer by hobby.

Who is the target audience?
  • This comprehensive tutorial caters to beginner data analysts, data science professionals or anyone looking to understand and build decision-making systems and recommendation engines and want a project based guide to do so.
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Curriculum For This Course
27 Lectures
Introduction to recommendation engines
4 Lectures 19:52

This video will brief introduction about the entire course

Preview 04:36

Learn to provide relevant suggestions based on datamining approaches

Recommendation engine definition

know about the different types of popular recommender systems and its use

Types of recommender systems

Learn to use the large user base and products which can scale easily and respond fast

Evolution of recommender systems with technology
Building your first recommendation engine
3 Lectures 15:39

Learn to install R Package in Rstudio to know how to load and format data

Preview 06:04

Learn to use the correlation value as the measure of similarity between two items in a matrix.

Calculating similarity between users

Learn to predict the unrated movies of a specified user using the ratings given by similar users

Predicting the unknown ratings for users
Recommendation engines explained
5 Lectures 22:37

Learn to develop new and different approaches

Preview 08:14

Learn to extract the features that represent the product

Content-based recommender system

Learn to capture the context information of the user and refine their suggestions accordingly.

Context-aware recommender system

Learn to implement the hybrid recommendation engine based on the problem statement and business needs

Hybrid recommender systems

Learn to build a probability modelusing the prior probabilities from the available data

Model-based recommender systems
Convolutional neural networks
7 Lectures 01:08:17

Learn to know the simple mathematical calculation that is applied between two vectors

Preview 10:35

Know about matrix factorization, alternate least squares, and singular value decomposition

Mathematical model techniques

Learn to predict the future outcomes based on given input parameters

Machine learning techniques

Learn linear classification, KNN classification, support vector machines, decision trees, and various ensemble methods

Classification models

Learn to know the process of grouping objects in one Group

Clustering techniques and dimensionality reduction

Learn to compute the similarity measure metric that data should be all numeric

Vector space models

Learn about cross validation and various popular evaluation metrics like root mean square error

Evaluation techniques
Building Collaborative Filtering Recommendation Engines
8 Lectures 46:16

The recommenderlab R package is a framework for developing and testing recommendation algorithms used to build recommendation engines. In this video, we’ll see how to installrecommenderlab

Preview 01:31

Like any other package available in R, recommenderlab also comes with default datasets. We need to know how to see available packages, methods, and algorithms. This video will guide us with that

Datasets available in the recommenderlab package

Now that we have started with the basics of data set, we must explore the data in more detail. This video will do exactly that and guide us in building and evaluating a recommender model

Exploring the dataset andbuilding user-based collaborative filtering

We use the same Jester5k dataset for the item-based recommender system as with UBCF. But, removing data of certain users and building IBCF system can be a bit tricky. Let’s see how we do that

Building an item-based recommender model

Now, we have seen implementations of user-based recommender systems and item-based recommender systems using the R package, recommenderlab. But, can we do the same with Python? In this video, we see theUBCF and IBCF implementations using the Python programming language

Collaborative filtering using Python

Let’s explore the MovieLens dataset and also prepare the data required for building collaborative filtering recommendation engines using python

Data exploration

If we observe the RMSE values in the model, we can see that the error is a bit higher. The reason may be that we have chosen all the users' rating information while making the predictions. In this video, we will cover finding the top-N nearest neighbors.

User-based collaborative filtering with the k-nearest neighbors

IBCF is very similar to UBCF but with very minor changes in how we use the rating matrix. But, still we must learn how to evaluate the model. This video guides in evaluating the model.

Item-based recommendations
About the Instructor
Packt Publishing
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