2 Main Goals
build a recommendation engine that recommends movies to users. We will be developing an Item Based Collaborative Filter, and then deploy it to a shinyapps.
Movie Recommendation System Project in R
Ezra Soterion Nugroho
4/10/2020
1 Brief Introduction
Have you ever been on an online streaming platform like Netflix, Amazon Prime, Voot? At some point each one of us must have wondered where all the recommendations that Netflix, Amazon, Google give us, come from. We often rate products on the internet and all the preferences we express and data we share (explicitly or not), are used by recommender systems to generate, in fact, recommendations.
This system is capable of learning the watching patterns and providing us with relevant suggestions. Having witnessed the fourth industrial revolution where Artificial Intelligence and other technologies are dominating the market, we surely have come across a recommendation system in our everyday life.
3 Collaborative Filtering
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, 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. It takes the information about the user as an input and then implement 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. There are two types of recommendation systems – Content-Based Recommendation System and Collaborative Filtering Recommendation. In this project of recommendation system in R, I will work on a collaborative filtering recommendation system and more specifically, ITEM based collaborative recommendation system. By looking at the items in common, this type of algorithm will basically predict the rate of a movie for a user who hasn’t watched it yet, based on the similar users’ rates.
4 Summary
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. I choose this projects because recommendation based porjects will be very relevant to the current industry and certainly can be implemented in many industrial areas.