The main aim here is to build a recommendation engine that recommends movies to users. I shall be developing an Item Based Collaborative Filter using recommenderlab package.
A recommendation system provides suggestions to the users through a filtering process that is based on user preferences and browsing history. The information about the user is taken as an input. The information is taken from the input that is in the form of browsing data. This information reflects the prior usage of the product as well as the assigned ratings. A recommendation system is a platform that provides its users with various contents based on their preferences and likings. A recommendation system takes the information about the user as an input. The recommendation system is an implementation of the machine learning algorithms.
A recommendation system also finds a similarity between the different products. For example, Netflix Recommendation System provides you with the recommendations of the movies that are similar to the ones that have been watched in the past. Furthermore, there is a collaborative content filtering that provides you with the recommendations in respect with the other users who might have a similar viewing history or preferences. There are two types of recommendation systems – Content-Based Recommendation System and Collaborative Filtering Recommendation. In this project of recommendation system in R, we will work on a collaborative filtering recommendation system and more specifically, ITEM based collaborative recommendation system.
In order to build our recommendation system, we have used the MovieLens Dataset. You can find the movies.csv and ratings.csv file that I have used in my Recommendation System Project here. This data consists of 105339 ratings applied over 10329 movies.
Recommendation Systems are the most popular type of machine learning applications; that are used in all sectors. They are an improvement over the traditional classification algorithms as they can take many classes of input and provide similarity ranking based algorithms to provide the user with accurate results. These recommendation systems have evolved over time and have incorporated many advanced machine learning techniques to provide the users with the content that they want.
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