Graph Based Recommendation Systems at eBay

Modeling taste with Cassandra

Affinity is based on user tastes, preferences, and interests

1

What is a taste profile?

Operational definition: the set of things you like and dislike

Stuff I like Stuff I don’t like

Challenge: how do you build a set of things you like and dislike
Operational definition: the taste profile for someone? 2

Inferring correlations
D 1) User A:
• Democrat
• Likes Arugula
2) User B:
C
• Republican
E
? • Dislikes Arugula
3) User C indicates:
• Democrat

What would we infer is User C’s affinity for
Arugula?

A
Answer: User C would like Arugula
B

4

Inferring correlations

Like arugula
User A

<3, 2.5>

<1,1>
Dislike Like
Obama Obama
User B

<-2,-1.5>

<-3,-3>
Dislike arugula

User C If someone’s affinity
for Obama is 2.0,
<2,?>
what is their affinity
for arugula?

5

Discovering latent factors
Obama
Liberal
Arugula <5, 5>
Like arugula
<4, 4>
User A

<3, 2>

<1,1>
Dislike Like
Obama Obama
User B

<-2,-1.5>

Iceberg
<-3,-3>
<-4, -4> Dislike arugula
GOP

<-5, -5> User C Predict 1.5 for how
much this person will
<2,1.5>
Conservative like arugula.

6

Taste space = many latent factors

<0.7, 4.4, -.1>
Liberal

<0.5, 2.4, -.4>
A
Extroverted

Masculine Feminine

<-0.5, -3.1, 0.1>
Introverted
B
Conservative

7

What is a taste profile profile?

Operational definition: a coordinate in taste space

Stuff I like (close to me in taste space) Stuff I don’t like (far away in taste space)

Operational definition: the set of things you like and dislike
Challenge: how do you calculate taste coordinates? 8

Calculating taste coordinates
D Edge weight = dot product of nodes
? <x, y>
to constrain similar items to be
2 <1, -1>
close to each other.
C Assume edge weights of:
E +2 = “love”
-2 = “hate”
2 <1, -0.5>
Democratic node must solve:
1*x -2*y = 2 (edge from A)
2
-2 1*x -1*y = 2 (edge from C)
A
Solution = <2, 0>
<1, -2> B
<-1, 2>

9

Updating taste coordinates

User A purchases a camera...

<1, -1>
<1, -0.5>
2 <1, -1>
2 <1, -1>
C
C
<-1, 0.5>
<-1, 0.5>
<1, -0.5>
2 <1, -0.5>

2
-2 2
2 -2
A 2
A
<1, -2> B
<0.75, -2.5> B
<-1, 2>
<-1, 2>

Resulting in blue coordinates changing.

v1 System overview - Model updates

1) Receive event
Rec. Updater (eg, Purchase)
Engine

3) Write user 2a) Write Purchase edge
and item 2b) Read other edges
coordinates for this user and item

Reco. DB Taste graph
User -> coord
Item -> coord

v1 System overview - Rec serving

1) Page load Rec. Updater
requests Engine
recommendations

2) Rec. engine
finds other
cameras close
to user’s
3) Recommendations coordinates
shown to user

Reco. DB Taste graph
User -> coord
Item -> coord

v1 Taste Graph data size

40 billion edges
2 billion item nodes
200 million user nodes

5TB of data, takes up 10TB with Replication Factor of 2

We expect this to quadruple next year as we get more events and add
new types of edges

13

v1 Taste Graph DB configuration

32 Linux machines
128GB RAM
1TB iSCSI SSD
10 GigE NIC

Cassandra version 1.0.8

8GB JVM heap space

Size-tiered compaction strategy

v1 Taste Graph schema

User Edges
(timestamp, edge_type, item_id) …
user_id <empty>
Item Edges
(timestamp, edge_type, user_id) …
item_id <empty>
User Nodes
tastevector
user_id 200 bytes (50 floats)
Item Nodes
tastevector
item_id 200 bytes (50 floats)

v1 Real-time taste updates

Edges and nodes read per second

v1 Real-time taste updates

Edges and nodes written per second

Questions?

tp@hunch.com

18

Graph Based Recommendation Systems at eBay

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Graph Based Recommendation Systems at eBay