Graph Gurus Episode 2: Building a Movie Recommendation Engine

Graph Gurus Episode 2
Building the Next Generation
Recommendation Engine
with a Graph Database, using TigerGraph’s GraphStudio visual SDK

© 2018 TigerGraph. All Rights Reserved
Welcome
● Attendees are muted but you can talk to us via Chat in Zoom
● We will have 10 min for Q&A at the end
● Send questions at any time using the Q&A tab in the Zoom menu
● The webinar will be recorded
● A link to the presentation and reproducible steps will be emailed
2
Developer Edition Download https://www.tigergraph.com/developer/

Today’s Moderator
● BS in Electrical Engineering and Computer
Science from UC Berkeley
● MS in Electrical Engineering from Stanford
University
● PhD in Computer Science from Kent State
University focused on graph data mining
● 15+ years in tech industry
3
Victor Lee, Director of Product Management

Today’s Guru
● BS in Computer Science from Fudan
University, China
● Master in Computer Science from Cornell
University
● With TigerGraph for over 3 years
● Co-author of GSQL query language
● Leading the design and development of
GraphStudio
4
Renchu Song, Engineering Manager

© 2018 TigerGraph. All Rights Reserved 5
Building a movie recommendation engine
1. Example data set
2. Defining movie recommendation graph schema
3. Defining data mapping
4. Loading data
5. Implementing movie recommendation algorithm
6. Executing query through RESTFul endpoint

1. Example Data Set - MovieLens 20M
• Size: 841M (after decompression)
• 20 million ratings applied to 27,000 movies by 138,000 real users.
• Download: https://grouplens.org/datasets/movielens/20m/

Data Format - CSV
movieId,title,genres
1,Toy Story (1995),Adventure|Animation|Children|Comedy|Fantasy
2,Jumanji (1995),Adventure|Children|Fantasy
3,Grumpier Old Men (1995),Comedy|Romance
4,Waiting to Exhale (1995),Comedy|Drama|Romance
5,Father of the Bride Part II (1995),Comedy
6,Heat (1995),Action|Crime|Thriller
7,Sabrina (1995),Comedy|Romance
8,Tom and Huck (1995),Adventure|Children
9,Sudden Death (1995),Action
10,GoldenEye (1995),Action|Adventure|Thriller
11,"American President, The (1995)",Comedy|Drama|Romance
...
userId,movieId,rating,timestamp
1,2,3.5,1112486027
1,29,3.5,1112484676
1,32,3.5,1112484819
1,47,3.5,1112484727
1,50,3.5,1112484580
1,112,3.5,1094785740
1,151,4.0,1094785734
1,223,4.0,1112485573
1,253,4.0,1112484940
1,260,4.0,1112484826
1,293,4.0,1112484703
...
ratings.csv (20000264 rows)movies.csv (27278 rows)

2. Defining Movie Recommendation Graph Schema
2 vertex types: person, movie 1 edge type: rate

3. Defining Data Mapping
movies.csvratings.csv
person(id) movie(id, title, genres)
rate(rating, rated_at)

4. Loading Data
• Loading speed
depends on machine
configuration
• Here it takes about 2
minutes.

5. Implementing Movie Recommendation Algorithm
Collaborative Filtering
“People who liked items which you like also like THESE OTHER ITEMS.”
I liked Wonder Woman
And Lady Bird
Group A also liked
Wonder Woman and
Lady Bird … and
The Shape of Water.

Input:
A person p, two integer parameters k1
and k2
Algorithm steps:
1. Find all movies p has rated;
2. Find all persons rated same movies as p;
3. Based on the movie ratings, find the k1
persons that have most similar tastes with p;
4. Find all movies these k1
persons rated that p hasn’t rated yet;
5. Recommend the top k2
movies with highest average rating by the k1
persons;
Output:
At most k2
movies to be recommended to person p.

Input:
and k2
Algorithm steps:
persons;
Output:
At most k2
p
…...
rate
rate
rate
PRatedMovies
PSet

Input:
and k2
Algorithm steps:
persons;
Output:
At most k2
p
…...
rate
rate
rate
PRatedMovies
PSet
…...
PeopleRatedSameMovies
rate
rate
rate
rate

Input:
and k2
Algorithm steps:
persons;
Output:
At most k2
p
…...
rate
rate
rate
PRatedMovies
PSet
…...
rate
rate
rate
rate

Input:
and k2
Algorithm steps:
persons;
Output:
At most k2
p
…...
rate
rate
rate
PRatedMovies
PSet
…...
rate
rate
rate
rate
…...
rate
rate
rate
rate

Input:
and k2
Algorithm steps:
persons;
Output:
At most k2
p
…...
rate
rate
rate
PRatedMovies
PSet
…...
rate
rate
rate
rate
…...
rate
rate
rate
rate
RecommendedMovies

GSQL Edge Block And Accumulator
Learn more at GSQL webinar: https://www.youtube.com/watch?v=EslAkGAEbFs
Person:s movie:mrate:r
r1{ rating = 2.5 }
r2 { rating = 4.5 }
r3 { rating = 0.5 }
r4 { rating = 5.0 }
r6 { rating = 5.0 }
r7 { rating = 4.0 }
@avgRating = 0
AvgAccum<double> @avgRating;
...
// People = { Tom, Emily, Dan }
...
AvgMovieRating =
SELECT m
FROM People:s -(rate:r)-> movie:m
WHERE r.rating >= 1.0
ACCUM m.@avgRating += r.rating
POST-ACCUM m.@avgRating += 1
ORDER BY m.@avgRating DESC
LIMIT 2;
Tom
Emily
Dan
@avgRating = 0
@avgRating = 0
@avgRating = 0
@avgRating = 0
@avgRating = 0
@avgRating = 0
Dunkirk
The Matrix
Zootopia
Star War
r5 { rating = 4.0 }

r1{ rating = 2.5 }
r2 { rating = 4.5 }
r3 { rating = 0.5 }
r4 { rating = 5.0 }
r6 { rating = 5.0 }
r7 { rating = 4.0 }
@avgRating = 0
...
...
AvgMovieRating =
SELECT m
LIMIT 2;
Tom
Emily
Dan
@avgRating = 0
@avgRating = 0
@avgRating = 0
@avgRating = 0
@avgRating = 0
@avgRating = 0
Dunkirk
The Matrix
Zootopia
Star War
r5 { rating = 4.0 }

r1{ rating = 2.5 }
r2 { rating = 4.5 }
r4 { rating = 5.0 }
r6 { rating = 5.0 }
r7 { rating = 4.0 }
@avgRating = 0
...
...
AvgMovieRating =
SELECT m
LIMIT 2;
Tom
Emily
Dan
@avgRating = 0
@avgRating = 0
@avgRating = 0
@avgRating = 0
@avgRating = 0
@avgRating = 0
Dunkirk
The Matrix
Zootopia
Star War
r5 { rating = 4.0 }
r3 { rating = 0.5 }

r1{ rating = 2.5 }
r2 { rating = 4.5 }
r3 { rating = 0.5 }
r4 { rating = 5.0 }
r6 { rating = 5.0 }
r7 { rating = 4.0 }
@avgRating = 2.5
...
...
AvgMovieRating =
SELECT m
LIMIT 2;
Tom
Emily
Dan
@avgRating = avg(4.5 + 5.0) = 4.75
@avgRating = 4.5
@avgRating = 4.0
@avgRating = 0
@avgRating = 0
@avgRating = 0
Dunkirk
The Matrix
Zootopia
Star War
r5 { rating = 4.0 }

r1{ rating = 2.5 }
r2 { rating = 4.5 }
r3 { rating = 0.5 }
r4 { rating = 5.0 }
r6 { rating = 5.0 }
r7 { rating = 4.0 }
...
...
AvgMovieRating =
SELECT m
LIMIT 2;
Tom
Emily
Dan
@avgRating = 0
@avgRating = 0
@avgRating = 0
Dunkirk
The Matrix
Zootopia
Star War
r5 { rating = 4.0 }
@avgRating = 2.5 + 1 = 3.5
@avgRating = 4.75 + 1 = 5.75
@avgRating = 4.5 + 1 = 5.5
@avgRating = 4.0 + 1 = 5.0

r1{ rating = 2.5 }
r2 { rating = 4.5 }
r3 { rating = 0.5 }
r4 { rating = 5.0 }
r6 { rating = 5.0 }
r7 { rating = 4.0 }
...
...
AvgMovieRating =
SELECT m
LIMIT 2;
Tom
Emily
Dan
@avgRating = 0
@avgRating = 0
@avgRating = 0
Dunkirk
The Matrix
Zootopia
Star War
r5 { rating = 4.0 }
@avgRating = 3.5
@avgRating = 5.75
@avgRating = 5.5
@avgRating = 5.0

Step 1. Find all movies p has rated;
p
…...
rate
{ rating = 3.5 }
rate
{ rating = 5.0 }
rate
{ rating = 3.0 }
PRatedMovies
PSet
{ @rated = true, @ratingByP = 3.5 }

Step 2. Find all persons rated same movies as p
…... …...
PRatedMovies PeopleRatedSameMovies
@ratingByP = 3.5
@ratingByP = 5.0
@ratingByP = 3.0
rate
rate
rate
rate
rate
rate

Step 3. Find the k1
persons that have most similar tastes with p.
A = [3.5, 3.0]
B = [0.5, 5.0]
@lengthASqr = 3.5 * 3.5 + 3.0 * 3.0 = 21.25
@lengthBSqr = 0.5 * 0.5 + 5.0 * 5.0 = 25.25
@dotProductAB = 3.5 * 0.5 + 3.0 * 5.0 = 16.75
@cosineSimilarity = 16.75 / sqrt(21.25) / sqrt(25.25) = 0.72311

Step 3. Find the k1
…...
@ratingByP = 3.5
@ratingByP = 5.0
@ratingByP = 3.0
@cosineSimilarity = 1
@lengthASqr = 3.5 * 3.5 + 3.0 * 3.0 = 21.25
@lengthBSqr = 0.5 * 0.5 + 5.0 * 5.0 = 25.25
@dotProductAB = 3.5 * 0.5 + 3.0 * 5.0 = 16.75
@cosineSimilarity = 16.75 / sqrt(21.25) / sqrt(25.25) = 0.72311
@cosineSimilarity = 0.99753
…...
rating = 3.5
rating = 5.0
rating = 0.5
rating = 5.0
rating = 5.0
rating = 3.5

Step 3. Find the k1
…...
@cosineSimilarity = 1
…...

Step 4. Find all movies these k1
persons rated that p hasn’t rated yet.
…... …...
rate
RecommendedMovies
rate
rate
rate
rate
@rated = false
@rated = false
@rated = false

…... …...
rating = 2.5
RecommendedMovies
rating = 5.0
rating = 4.5
rating = 4.5
rating = 1.0
@recommendScore = 1.75
Step 5. Recommend the top k2
movies with highest average rating by k1
persons.

Step 5. Recommend the top k2
movies with highest average rating by k1
persons.
…... …...
rating = 2.5
RecommendedMovies
rating = 5.0
rating = 4.5
rating = 4.5
rating = 1.0

curl -X GET 'http://<MACHINE-IP>:9000/query/MyGraph/RecommendMovies?p=238&k1=50&k2=20' |
python -m json.tool
6. Executing query through RESTFul endpoint

Other Recommendation Algorithms
Another example:
https://doc.tigergraph.com/GSQL-Demo-Examples.html
● Content-based
○ Considers the genres of the movies
Other approaches and formulas
● Profile the users and focus on similar users
● Other scoring systems
Production systems using TigerGraph - Wish.com
delivering real-time cross-sell and up-sell recommendations to over
300 million users and driving billions in annual revenue
Person Movie Genre

Q&A
Please send your questions via the Q&A menu in Zoom
35

Episode 3: Sept 12
Detecting Fraud and Money Laundering in Real-Time with a Graph DB
https://info.tigergraph.com/graph-gurus-3
36
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webinars-and-events/

Additional Resources
37
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https://www.tigergraph.com/download/
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https://github.com/tigergraph/ecosys/tree/master/guru_scripts
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Graph Gurus Episode 2: Building a Movie Recommendation Engine

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