Artfinder is the art marketplace. Users can buy original paintings, sculptures, limited edition prints and photography from independent artists. David Dyball, DevOps Engineer, joins us for this week's online meetup to talk through how they're using Neo4j to make recommendations to user about which pieces of art they might like to buy. There'll be lots of Cypher queries! Meetup link: https://www.meetup.com/Neo4j-Online-Meetup/events/238681208/

1. Our Graph
Artist
User
Product
LIKES
LIKES
Our first model

2. First Try….
Took 18 minutes per-user…. ouch!

3. Optimisations
● It didn’t take much for one user to traverse ~1M nodes & 14M relationships in one query
● Identification/limiting of supernodes
(an individual node with more than 200K relationships)
● Limiting scope to only use the most recent ~400 [:LIKES] per user
● Pre-calculation of weighted User-to-User relationships
● Differentiating between actions/relationships in the graph ([:BOUGHT] vs. [:LIKES])

4. Our Graph
Artist
User
Product
BO
U
G
H
T_FR
O
M
FO
LLO
W
S
LIKES
BO
U
G
H
T
SIM
ILAR
_TO
_U
SER
Our second model

5. Pre-calculation of Similar Users

6. End result is a weight that identified the top {limit} users that are likely to influence
the given user. These are then stored back in the graph under
[:SIMILAR_TO_USER] relationships

7. Calculation of Recommendations
Being able to pass in a {weights} array made request-time tweaking a lot easier.

8. Me
My Recommendations

9. Tips / Things Learnt
Artist
User
Product
BO
U
G
H
T_FR
O
M
FO
LLO
W
S
LIKES
BO
U
G
H
T
SIM
ILAR
_TO
_U
SER
Be prepared to iterate on your model
● Make sure iteration is part of your
development process
Ask for Help from the Community!
● Slack is your friend!
https://neo4j-users.slack.com
● The Neo4J team are ALWAYS
answering questions on
StackOverflow

10. HAS_STYLE
H
AS_SU
BJEC
T
HAS_CATEGORY
VIEW
ED
The Future
Artist
User
Product
BO
U
G
H
T_FR
O
M
FO
LLO
W
S
LIKES
BO
U
G
H
T
VISU
ALLY_SIM
ILAR
SIM
ILAR
_TO
_U
SER
Style
Subject
Category
VIEW
ED