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Behavioral Targeting @ Scale
- How did we know that this Ad was relevant for you ?
Savin Goyal
Sivasankaran Chandrasekar

ADVERTISER ROCKET FUEL
200+
RTB advertising
supply partners
50+ Mn
Websites
50+ Bn
Daily impressions
3B WW CONSUMERS
100,000+ DEVICES

Exchanges
Ad
Exchange
Rocket Fuel Platform
Auto
Optimization
Real-Time
Bidding
Agencies
Data Partners
Display Advertising Ecosystem

Bid on Ad
User
Data
Bid Request
Rocket Fuel
Winning AdAd Request
Ad Served to User
Page RequestWeb Browser
Rocket Fuel Platform
Smart Ad Servers
Response
Prediction
Models
1
8
2 7
Calculate
Propensity
Score
5User
Engagement
Recorded
9 User Engages with Ad
Publishers
Refresh
learning
Campaign &
Audience
Data
4
Qualify
Campaign
10
3
6
Data Partners
Exchange Partners
Programmatic Buying

1.25
$2.11
$1.26
$2.78
$1.256
$1.809
$2.42
1.25
$2.11
$1.26
$2.78
$0.586
$2.009
1.25
$2.11
$1.26
$2.78
$1.56
$0.00
Site/PageGeo/WeatherTime of DayBrand AffinityUser
[ + ][ + ]
Real Time Auction

Goal:
Leads
& sales
Goal:
Coupon
downloads
Goal:
Brand
awareness
Site/PageGeo/WeatherTime of DayBrand AffinityDemo
Impression Scorecard
Demo
Brand Affinity
Time of Day
Geo/Weather
Site/Page
Ad Position
In-market
Behavior
Response
Demo
Brand Affinity
Time of Day
Geo/Weather
Site/Page
Ad Position
In-Market
Behavior
Response
X
Demo
Brand Affinity
Time of Day
Geo/Weather
Site/Page
Ad Position
In-Market
Behavior
Response
+100
+40
-20
+20
+15
+10
+40
+35
+9.7%
+40
-70
-20
+10
+15
-25
-40
-18
+0.7%
+10
-10
-20
+20
+10
-35
-25
+10
+1.4%
X
Real Time Auction

Scalable Predictive Models
Age/Gender
Occupation
IncomeEthnicity
Purchase Intent
Online
Purchases
Offline
Purchases
Browsing
Behavior
Site Actions
Zip CodeCity/DMA
Search
Sites
Search
Categories
Recency
Search
Keywords
Web Site/Page
Referral URL
Site
Category
Bizographics
Social
Interests Lifestyle
Positive Lift
Marginal Impact
Negative Lift
-7
+17
X
-2
+8
+14
X
-9
-13
-12
X
+19
+13
+11
X
+11
X
X
X
+25
+6
X
-7 +17
-2
+28
X
+11
X
X
-9
+14
+17 +19
+8 +11
X
X
-9
+17
-23
+6
X
+17
-7
X
-2
-13
-12
X
+13
+6
+11
X
X
X
-9 X
+17
X
+19
+8
+14
+18
-23
+17
-12
+11
-9
+8 +14
X
+11
-13
-12
+13
+11
X
X
-7
+17 +8
+18X
+11
X -12-10
+6
+14
X
+8
+11
-10+13
+28 +6
+13
+19
X
+8
+11
-10
+13
-12
+17
X
-7
+8
X
Automated Feature Selection
 Infinite number of models
 Determine perfect model size
 Balance past data fit
and future generalization
Learn-Test-Refine
 Automatically learn from
each response
 Cross-validate - A / B testing
infrastructure
 Training pipeline

5 B
6 B
50 B
Facebook likes
Searches on Google
Events processed by Rocket Fuel
Requests per day
Throughput

Rocket Fuel Scale
 34,474 CPU Processor Cores
 2655 servers
 187.4 Teraflops of computing
 188 Terabytes of memory
 13X the memory of Jeopardy-
winning IBM Watson
 42 Petabytes of storage
 106X the data volume of entire
Library of Congress

200 Servers 1400 Servers
5 PB
41 PB
8x
Data Warehouse Growth

Behavioral Targeting

 Leverage online activities on the web to learn about user’s
 Long Term Interests
 User is interested in luxury cars
 Short Term Interests
 User is looking for a pizza right now
 Expand user set beyond retargeting
 Explore v/s Exploit
 Identify relevant users even if they have never been targeted
previously

Behavioral Targeting @ Rocket Fuel
Label Data
Train
Model
Back Test
Calibrate
Training
Events
Pixel
Stream
Ad Logs
BT Features
(HBase)
Feature
Generation
Score
Profiles
Profile
Generation
Scoring
Ad Serving Data Centers Model

Hadoop/HBase @ Rocket Fuel
 Cluster Highlights
 650+ Slaves (64 GB + 12 *3 TB)
 20 PB Storage
 HA Name Node Set Up
 9k Map Slots + 5.5k Reduce Slots
 Co-located to run HBase for offline processing
 HBase 0.94.15
 5 Node ZooKeeper quorum
 Monitoring with OpenTSDB
 Dual Master Setup

Behavioral Targeting @ Rocket Fuel
bmw.com 11:23
Cars 11:23
pizzahut.com 11:26
Food 11:26
honda.com 11:27
Cars 11:27
30 minutes
honda.com 11:27 Recent 6 hours: 5 Between 6 and 12 hours: 3 Between 12 hours and …
Food 11:26 Recent 6 hours: 2 Between 6 and 12 hours: 7 Between 12 hours and …
Read events of
last N days
Recency
Frequency
Others..
Behavioral Targeting Profile
11:23 11:26 11:27

HBase Data Model
11:23ABCD06EFG
2014060416:site:bmw.com 2014060416:category:food
11:26
row_key: user_id
Single Column Family “u”
Column Qualifier:
<date><hour>:<type>:<value>
Cell Value: [Protobuf]
Most recent timestamp, Event details
relative to timestamp
Event details relative to 11:23 Event details relative to 11:26
• Efficient look up for a given user
• Access range of events by event date, hour and type

Key Challenges
User Profile Freshness Scaling Issues Pipeline Failures

User Profile Freshness
 Strict latency requirements
 Recent activity much better
predictor
Solutions -
 Staggered Pipelines
 Real Time Behavioral Targeting

Staggered Pipelines
Extract Score Filter Upload
Extract Score Filter UploadSource Data

Real Time

Batched Profile
Blackbird – HBase instance tuned for 2ms latencies
Refreshed
every N hours
Real Time Behavioral Targeting
Offline BT
Pipeline
BT Profile
Ad Servers Merge Profiles
Logs
Blackbird
Online Profile
Record events for users
in real time
Request
Response

Batched Updates vs. Real Time Updates
Event Granularity
Aggregated over
several hours/days
Raw recorded events
appended for recent
N hours
Processing Load
Requires minimal CPU
processing
Needs aggregation
on-the-fly
Disk Footprint
Compact
representation
captures several days
Strict limits to ensure
read times are
acceptable
Coverage All interactions
Only interactions at a
data center
 Real Time Profile updated in milliseconds
 Batched Profile refreshed every N hours
Batched Profile Real Time Profile

Scaling Issues
 3X growth in events processed/year
 First Party Data
 App Interactions
 Geo-location Data
 …
 Case Studies
 HBase Region Hot-spotting
 Network Bandwidth Troubles

HBase Region
Hot Spotting

HBase
Region
HBase Region Hot-spotting
High Write Load
HBase
Region
HBase
Region
Region Split (painful!)
Some users more active than others
No control on user id’s generated
Still
problematic
Non-uniform
distribution!

 Uneven write-load distribution
 Non-Uniform Row Key Distribution
 Salt row key’s to ensure uniform distribution
 Fixed length hashed prefix

Murmur hash
based prefix
Original User ID
 Uniform pre-splits

 Don’t stop at salting
 Map input splits configured for region boundaries
Region 1
x03x85x1ExB8ZZZZZZ
Region 2
x07x5CxF5xC2928ZZ
Region m
xFFxAEx14xE1Z28ZZ
1234557
1234568
1234579
1234583
1234594
..
..
..
..
ZZAHT654
ZZZGT934
ZZZZNGA2
ZZZZKLO1
Key
Partitioner
‘k’ splits ‘m’ regions‘m’ splits
x01x85x1ExB811ZKL1
x01x86x1ExB8129542
..
x03x85x1ExB8ZZZKL1
x05x35x9Ex18087KL1
x06x86x1ExB8AHV24
..
x07x5CxF5xC16534Z
xEBx27x92x1508RKL1
xFEx86x1ExB8AHV24
..
xFFxAEx14x126534Z

HBase Key Partitioner
 As many splits as regions to maximize parallelism
 Key Partitioner (MR) –
 Reads region boundaries of HBase table
 Salts and sorts row key accordingly
 Multiple Output Format to optimize reduce phase
 Each generated split file corresponds to a single region
 Drastically reduces read latencies

Network Bandwidth
Troubles

Data Center Expansion

Network Bandwidth Constraints
 Consistently overshot bandwidth limit during uploads
 All sorts of delays (Redis, MySQL, Blackbird…)
 Bidding hampered

Solutions
 Intelligent storage – protobufs everywhere
 Throttle writes
 Geo-splitting

Geo Splitting

Geo-splitting
 Tag user’s location history & predict future data center visits
 ⨍(dc, geo_history, bt_profile)
 A separate workflow periodically generates geo-split rules:
 Clusters users & analyzes migration patterns
 Ensures maximal look-up coverage of profiles
 Minimizes total number of profiles stored
 Ensures efficient use of resources, with minimal impact on perf

Geo-splitting
Label Data
Train
Model
Back Test
Calibrate
Training
Events
Pixel
Stream
Ad Logs
BT Features
(HBase)
Feature
Generation
Score
Profiles
Profile
Generation
Scoring
Ad Serving Data Centers Model
Cluster
Users
Analyze
Patterns
Generate
Rules
Geo-split

Quick Recovery From Failures
 Break pipeline into short payloads
 Fail fast, recover fast!
 Actionable alerts, cut down noise

Quick Recovery From Failures
 Materialize data as frequently as possible
 Cross system fault tolerance
 Idempotency
 Backfill at EOD to plug holes if needed

Shout-outs!

We Are Hiring!

Questions ?
Thank You!
Sivasankaran Chandrasekar
chandra@rocketfuel.com
Savin Goyal
savin@rocketfuel.com

We are hiring! (as always)
http://rocketfuel.com/careers
savin@rocketfuel.com
chandra@rocketfuel.com

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Editor's Notes