Movie Recommendation System

A recommendation system is a type of information filtering system that attempts to predict how a user would rate or prefer an item. In layman’s terms, it is an algorithm that suggests relevant items to users. For example, on Netflix, which movie to watch, on e-commerce, which product to buy, on Kindle, which book to read, and so on.

Goal: In this implementation, when the user searches for a movie we will recommend the top 5 similar movies using our movie recommendation system.

The dataset was provided by MovieLens, a movie recommendation service. It includes the movies and the rating scores made for these movies. contains. It contains100,000 ratings (1–5) from 943 users on 1682 movies.

Let’s get this data into Python. We may use several of the files in the ml-100k.zip file. To obtain a sense of the data, let’s load the three most important files.

We have imported the basic libraries. And we will also upload the data.

This reconfirms that there are 100003 users and we have 4 features. This confirms that there are 100K ratings for different user and movie combinations. Also, notice that each rating has a timestamp associated with it.

To better understand the data, EDA entails generating summary statistics for numerical data in the dataset and generating various graphical representations.

Let’s explore the data a bit and get a look at some of the best-rated movies.

By looking at the most rated movies, it might happen only a few users have given the rating for movies. So, let's look at rating counts it means how many users have given ratings for particular movies.

Here we can clearly, see that movie “Star Wars” has the most-watched movies from this dataset.

By looking at the graph we say that there are some outliers, but the graph roughly follows the normal distribution curve. And most user has given a rating of movies between 3 to 4.

Now let’s create a matrix that has the user ids on one access and the movie title on another axis. Each cell will then consist of the rating the user gave to that movie. Note there will be a lot of NaN values because most people have not seen most of the movies.

Let's find the Similar movies of “Star Wars (1977)”

Here we are going to find the movies more similar to “Star Wars (1977)” with the help of correlation. Higher the correlation more similarity between them.

Assume we have two vectors. If the vectors are almost parallel, i.e. the angle between them is zero, we can conclude that they are “similar,” because cos(0)=1. If the vectors are orthogonal, we can say they are independent or NOT “similar,” because cos(90)=0.

KNN calculates the distance from all points in the proximity of the unknown data and filters out the ones with the shortest distances to it. As a result, it’s often referred to as a distance-based algorithm.

Now we will select the random movies from the pivot table:

Here we have used cosine similarity to find similar movies to recommend to the users. Therefore, nearest neighbors are those data points that have minimum distance in feature space from our new data point.

We talked through the process of creating a basic recommendation engine in Python using Correlation and cosine similarity in this article. We began by learning the principles of recommendation authoring. Then, for the sake of experimentation, we loaded the MovieLens 100K data set.

Having followed that, we created a simple popularity model in which the most popular movies for each user were recommended by using correlation and also used cosine similarity to find similar movies.

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