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Version: 2.10.1

Movie recommendation system

This article is a part of a series intended to show users how to use Memgraph on real-world data and, by doing so, retrieve some interesting and useful information.

We highly recommend checking out the other articles from this series which are listed in our tutorial overview section, where you can also find instructions on how to start with the tutorial.

Introduction​

This example shows how to implement a simple recommendation system with openCypher in Memgraph. First, we will show how to perform simple operations, and then we will implement a query for the movie recommendation.

Data model​

In this example, we will use MovieLens dataset, which consists of 9742 movies across 20 genres. There are three types of nodes: Movie, User and Genre. Movie nodes have properties: id and title. Users have an id property, while genres nodes have a property: name

Each movie can be connected with :OF_GENRE relationship to different genres. A user can rate some movies. Rating is modeled with :RATED relationship and this relationship has a property rating — float number between 0 and 5.

Movies

Exploring the dataset​

To follow this tutorial, download the Memgraph Platform. Once you have it up and running, open Memgraph Lab web application within the browser on localhost:3000 and navigate to Datasets in the sidebar. From there, choose the dataset MovieLens: Movies, genres and users and continue with the tutorial.

Example queries​

1. List first 10 movies sorted by title:

MATCH (movie:Movie)
RETURN movie
ORDER BY movie.title
LIMIT 10;

2. List 15 users from the dataset:

MATCH (user:User)
RETURN user
LIMIT 15;

3. List 10 movies that have Comedy and Action genres and sort them by title: 

MATCH (movie:Movie)-[:OF_GENRE]->(:Genre {name:'Action'})
MATCH (movie)-[:OF_GENRE]->(:Genre {name:'Comedy'})
RETURN movie.title
ORDER BY movie.title
LIMIT 10;

4. Average score for Star Wars: Episode IV - A New Hope (1977) movie:

MATCH (:User)-[r:RATED]->(:Movie {title:"Star Wars: Episode IV - A New Hope (1977)"})
RETURN avg(r.rating)

5. Return the first 10 movies that are ordered by rating:

MATCH (:User)-[r:RATED]->(movie:Movie)
RETURN movie.title, avg(r.rating) AS rating
ORDER BY rating DESC
LIMIT 10;

6. Create a new user and rate some movies:

CREATE (:User {id:1000});

7. Check if new user is created:

MATCH (user:User{id:1000})
RETURN user;

8. Create some ratings for the user:

MATCH (u:User {id:1000}), (m:Movie {title:"2 Guns (2013)"})
MERGE (u)-[:RATED {rating:3.0}]->(m);
MATCH (u:User {id:1000}), (m:Movie {title:"21 Jump Street (2012)"})
MERGE (u)-[:RATED {rating:3.0}]->(m);
MATCH (u:User {id:1000}), (m:Movie {title:"Toy Story (1995)"})
MERGE (u)-[:RATED {rating:3.5}]->(m);
MATCH (u:User {id:1000}), (m:Movie {title:"Lion King, The (1994)"})
MERGE (u)-[:RATED {rating:4.0}]->(m);
MATCH (u:User {id:1000}), (m:Movie {title:"Dark Knight, The (2008)"})
MERGE (u)-[:RATED {rating:4.5}]->(m);
MATCH (u:User {id:1000}), (m:Movie {title:"Star Wars: Episode VI - Return of the Jedi (1983)"})
MERGE (u)-[:RATED {rating:4.5}]->(m);
MATCH (u:User {id:1000}), (m:Movie {title:"Godfather, The (1972)"})
MERGE (u)-[:RATED {rating:5.0}]->(m);
MATCH (u:User {id:1000}), (m:Movie {title:"Lord of the Rings: The Return of the King, The (2003)"})
MERGE (u)-[:RATED {rating:4.0}]->(m);
MATCH (u:User {id:1000}), (m:Movie {title:"Aladdin (1992)"})
MERGE (u)-[:RATED {rating:4.0}]->(m);
MATCH (u:User {id:1000}), (m:Movie {title:"Pirates of the Caribbean: The Curse of the Black Pearl (2003)"})
MERGE (u)-[:RATED {rating:4.5}]->(m);
MATCH (u:User {id:1000}), (m:Movie {title:"Departed, The (2006)"})
MERGE (u)-[:RATED {rating:4.0}]->(m);
MATCH (u:User {id:1000}), (m:Movie {title:"Texas Rangers (2001)"})
MERGE (u)-[:RATED {rating:2.0}]->(m);
MATCH (u:User {id:1000}), (m:Movie {title:"Eve of Destruction (1991)"})
MERGE (u)-[:RATED {rating:1.0}]->(m);
MATCH (u:User {id:1000}), (m:Movie {title:"Sharkwater (2006)"})
MERGE (u)-[:RATED {rating:2.0}]->(m);
MATCH (u:User {id:1000}), (m:Movie {title:"Extreme Days (2001)"})
MERGE (u)-[:RATED {rating:1.5}]->(m);

9.Check all the movies user with id = 1000 has rated: 

MATCH (user:User {id:1000})-[rating:RATED]->(movie:Movie)
RETURN user, movie, rating

10. Recommendation system:

The idea is to implement simple memory based collaborative filtering.

Let's recommend some movies for user with id = 1000:

MATCH (u:User {id:1000})-[r:RATED]-(m:Movie)
      -[other_r:RATED]-(other:User)
WITH other.id AS other_id,
     avg(abs(r.rating-other_r.rating)) AS similarity,
     count(*) AS same_movies_rated
WHERE same_movies_rated > 2
WITH other_id
ORDER BY similarity
LIMIT 10
WITH collect(other_id) AS similar_user_set
MATCH (some_movie:Movie)-[fellow_rate:RATED]-(fellow_user:User)
WHERE fellow_user.id IN similar_user_set
WITH some_movie, avg(fellow_rate.rating) AS prediction_rating
RETURN some_movie.title AS Title, prediction_rating
ORDER BY prediction_rating DESC;

How does this query work?

This query has two parts:

  • Finding similar users
  • Predicting the score for some movie (recommendation)

In the first part, we are looking for similar users. First, we need to define similar users: Two users are considered similar if they tend to give similar ratings to the same movies. For the target user and some other user we are searching for the same movies:

MATCH (u:User {id:1000})-[r:RATED]-(m:Movie)-[other_r:RATED]-(other:User)
RETURN *;

If you try to execute the query above with added RETURN statement, you will get all potential similar users and movies they rated. But this is not enough for finding similar users. We need to choose users with the same movies and similar ratings:

WITH other.id AS other_id,
     avg(abs(r.rating-other_r.rating)) AS similarity,
     count(*) AS same_movies_rated
WHERE same_movies_rated > 2
WITH other_id
ORDER BY similarity
LIMIT 10;
WITH collect(other_id) AS similar_user_set

Here we calculate similarities as the average distance between the target user rating and some other user rating on the same set of movies. There are two parameters: same_movies_rated defines the number of same movies (more than 3) that the target user and other users need to rate, and similar_user_set represents the users that gave a similar rating to the movies that the target user has rated. These parameters enable extracting the best users for movie recommendations.

Now we have a similar user set. We will use those users to calculate the average rating value for all movies they rated in the database as prediction_rating variable, and return the best-rated movies order by prediction_rating variable.

MATCH (some_movie: Movie)-[fellow_rate:RATED]-(fellow_user:User)
WHERE fellow_user.id IN similar_user_set
WITH some_movie, avg(fellow_rate.rating) AS prediction_rating
RETURN some_movie.title AS title, prediction_rating
ORDER BY prediction_rating DESC;

We encourage you to play with some parameters, like same_movies_rated limit and similar_user_set size limit. You can also try to use different similarity functions, for example Euclidean distance:

sqrt(reduce(a=0, x IN collect((r.rating - other_r.rating) * (r.rating - other_r.rating)) | a + x)) AS similarity;

Here we use reduce function. Reduce function accumulate list elements into a single result by applying an expression. In our query, this function starts with 0 and sums up squared differences. collect function is used for putting squared differences into the list.