Find the items with the most similar preferences to movie Titanic, according to cosine similarity

Neo4j Graph Platform
1

Hi,
I've been trying to code the above r from the MOVIE Lens database and i dont really get how to do it. Can someone help? I just registered with the community and i dont really understand how this works, i would appreciate any help.

I tried the following code

MATCH (p1:Movie)-[x:RATED]->(Movie{title: 'Titanic'})<-[x2:RATED]-(p2:Movie)

WHERE p2 <> p1

WITH p1, p2, collect(x.rating) AS p1Ratings, collect(x2.rating) AS p2Ratings

WHERE size(p1Ratings) > 10

RETURN p1.name AS from,

   p2.name AS to,

   gds.similarity.cosine(p1Ratings, p2Ratings) AS similarity

ORDER BY similarity DESC

2

Hello @miklov :blush:

This query should help you, I did on the Movie database (there is no result for this movie):

MATCH (m:Movie {title: 'The Da Vinci Code'})
WITH m, [(m)<-[r:REVIEWED]-(:Person) | r.rating] AS ratings_m
CALL {
    WITH m, ratings_m
    MATCH (n:Movie)
    WHERE m <> n
    WITH ratings_m, n, [(n)<-[r:REVIEWED]-(:Person) | r.rating] AS ratings_n
    WHERE size(ratings_n) > 0 AND size(ratings_m) = size(ratings_n)
    RETURN n, gds.similarity.cosine(ratings_m, ratings_n) AS similarity
}
RETURN m.title AS m1, n.title AS m2, similarity

Be careful, the lists of values in the similarity function must have the same size.

Regards,
Cobra

3

Hi miklov,

I know you've already gotten some help here, but there is a semantic error in your query that you may have overlooked:

MATCH (p1:Movie)-[x:RATED]->(Movie{title: 'Titanic'})<-[x2:RATED]-(p2:Movie)

p2 should be a User and not a Movie, I believe.

4

Hello Cobra,

Thank you very very much for the quick reply, appreciated. So the outcome should be (no changes, no records)? Also i have a few more questions on this database, would you be able to help?

Thank you very much,

Mikaella

5

The movie The Da Vinci Code has no similar movies so there is no result but this query can be transposed to other databases. The Movie database is not the best one to test GDS algorithms.

Feel free to ask your questions.

Regards,
Cobra

6

Thank you very much. I have send you a private message :slightly_smiling_face: