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.
Learn to provide relevant suggestions based on datamining approaches
know about the different types of popular recommender systems and its use
Learn to use the large user base and products which can scale easily and respond fast
Learn to install R Package in Rstudio to know how to load and format data
Learn to use the correlation value as the measure of similarity between two items in a matrix.
Learn to predict the unrated movies of a specified user using the ratings given by similar users
Learn to develop new and different approaches
Learn to extract the features that represent the product
Learn to capture the context information of the user and refine their suggestions accordingly.
Learn to implement the hybrid recommendation engine based on the problem statement and business needs
Learn to build a probability modelusing the prior probabilities from the available data
Learn to know the simple mathematical calculation that is applied between two vectors
Know about matrix factorization, alternate least squares, and singular value decomposition
Learn to predict the future outcomes based on given input parameters
Learn linear classification, KNN classification, support vector machines, decision trees, and various ensemble methods
Learn to know the process of grouping objects in one Group
Learn to compute the similarity measure metric that data should be all numeric
Learn about cross validation and various popular evaluation metrics like root mean square error
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
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
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
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
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
Let’s explore the MovieLens dataset and also prepare the data required for building collaborative filtering recommendation engines using python
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.
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.
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