@IndeedEng: Tokens and Millicents - technical challenges in launching Indeed around the world

Tokens and Millicents
Technical challenges in launching Indeed
around the world

Engineering Director
Dan Heller

what where
job title, keywords or company name city, state or zip code
software Find Jobsaustin

was wo
job title, keywords or company name city, state or zip code
produktionshelfer Jobs findenmünchen

キーワード勤務地
職種、キーワード、会社名など都道府県名または市区町村名
登録栄養士求人検索大阪

Αθήνα
τι που
τίτλος θέσης εργασίας, λέξεις-κλειδιά ή όνομα εταιρείας πόλη ή πολιτεία
βοηθός λογιστή Εύρεση θέσεων εργασίας

Software Engineer
Preetha Appan

ALL JOBS
Relevant
Jobs
Returned
Jobs

Precision: Positive Predictive Value
# Returned and relevant
# Returned

Precision
Job seeker searches for “architect”
usually means “building architect”

Precision
10 jobs returned:
8 building architect jobs
2 software architect jobs

Precision
10 jobs returned:
8 building architect jobs Relevant
2 software architect jobs Not Relevant
Precision: 8 / 10

Recall: Specificity
# Returned and relevant
# Relevant

Recall
Job seeker searches for “hr”
Jobs that mention “hr” or “human resources”
are both relevant to the job seeker.

Recall
10 jobs are relevant:
7 hr jobs
3 human resources jobs

Recall
10 jobs are relevant:
7 hr jobs Returned
3 human resources jobs Not Returned
Recall: 7 / 10

Improving Recall
in Job Search

Senior Software Engineer - Search
Indeed - Austin, TX
Indeed.com is seeking a Senior Software Engineer
responsible for the information retrieval system that
powers Indeed’s job search website.
If you are an engineer who's passionate about building
innovative products...
Job Description - English

Senior Software Engineer - Search
Indeed - Austin, TX
Indeed.com is seeking a Senior Software Engineer
responsible for the information retrieval system that
powers Indeed’s job search website.
If you are an engineer who's passionate about building
innovative products...
Tokenization

Inverted Index
● Like index in the back of a book
● words = tokens, page numbers = doc ids

Inverted Index
Token Job A Job B Job C
assistant ✔
developer ✔
engineer ✔
lawyer ✔ ✔
paralegal ✔ ✔
retrieval ✔

Inverted Indexes
Allow you to:
● Quickly find all documents containing a
token
● Perform boolean queries, e.g “java AND
developer”

Apache Lucene
Open source inverted index implementation
Fast, widely used

Tokenization with Lucene
StandardAnalyzer
● Uses space and punctuation to determine
token boundaries

StandardAnalyzer - problems
● C++, C# → C
● O’Reilly → O, Reilly

Tokenization with Lucene
JobAnalyzer
● forked StandardAnalyzer
● Modified it to make it work for jobs

Secrétaire
Saclay
Au sein de la direction de la Qualité et de l'Environnement (DQE)
vous seconderez la secrétaire-assistante. Vos principales
missions seront :
- organisation de réunions
- l'accueil téléphonique
- la gestion des missions ..
Job Description - French

Job Description - Chinese
岗位描述:
1、全厂电气设备的日常检查、记录，在操作工或
主操的指导下进行工艺操作.
2、现场液体充装，现场充装安全的管理.
3、负责现场工作环境的整洁.
...

Job Description - Japanese
ちょっと想像してみてください。
ご近所のサーティワンにあなたが企画開発

Kanji

Kanji
Hiragana

Kanji Hiragana Katakana

Chinese using JobAnalyzer
全厂电气设备的日常检查、记录，
在操作工或主操的指导下进行工艺操作.

“Daily inspection of electrical equipment plant-wide”

JobAnalyzer in CJK =
Poor recall

医療事務兼検査助手
medical

????

affairs

“東京都”
Tokyo prefecture

“東京都”
Tokyo prefecture
東京都東京都

東京都
“東京都”
Tokyo prefecture
東京都
Tokyo

東京都東京都
“東京都”
Tokyo prefecture
Tokyo Kyoto

Bigram tokenizer Drawbacks
● Poor precision

Bigram tokenizer Drawbacks
● Poor precision
● Too many terms

Accent and gender normalization
● secrétaire, secretaire

● vendeur, vendeuse

● vendeur, vendeuse
● promotor@s

Language Detection options
● HTTP Content-Language response header
○ Most sites don’t provide this header
○ May not be accurate

Language Detection - ICU4J
● ICU4J’s CharsetDetector
○ Works well for languages with single byte
encoded characters
○ Detect that language is one of
Danish, Dutch, English, French,
German, Italian, Portuguese, Swedish

Naive Bayesian classifier
● Features - words
● Strong independence assumption
● Class label - language

Naive Bayesian Language detector
Hand labelled training data in each language

For each language, calculate P(wi
ϵ Lj
)
● P(“experience” ϵ en) = 0.85

P(w1
ϵ Lj
) * P(w2
ϵ Lj
) * P(w3
ϵ Lj
)*..

● 100% accurate
● Used in:
○ Thai
○ Greek
○ Korean
○ Hebrew
Using Unicode Blocks

CJ language detection
● Strongly weight Hiragana and Katakana
● Some characters (Kanji) common between
Chinese and Japanese
● p(卒 ϵ ja) = 0.99 p(卒 ϵ zh) = 0.000001

Language Results
● Did cross validation on hand labeled testing
data
● 99% accurate for text > 30 characters
○ Average job description is 200 characters
● Fast - 0.6ms per job

Other language detectors
Google - https://code.google.com/p/cld2/

CJK tokenizers
● Dictionary-based
● Statistical model & dictionary

Dictionary-based tokenizers
● Dictionary of words in language
● Scan input sentence, return all possible
tokenizations

北京大学生前来应聘
Beijing

Beijing college students

Beijing college students come to

Beijing college students come to apply jobs

Peking University

Peking University before death

Peking University before death come to apply jobs

北京大学
Peking University
中国
China
生前
before death

北京大学
Peking
University
生前
before death
北京
Beijing
大学生
college
student

✔
✘
北京大学
Peking
University
生前
before death
北京
Beijing
大学生
college
student

CJK tokenizers
● Chinese - Imdict
● Japanese - Sen
● Korean - LuceneKorean

Chinese tokenization
http://nlp.stanford.edu/projects/chinese-nlp.shtml

● Different rules per language around
○ Gender
○ Plurals
○ Collation
More recall challenges

Apply language specific rules to transform words
to canonical form
Use detected language

What is stemming?
the process of turning multiple variations of a
word into a single equivalent root

Stemming examples
● driver, drivers → driver
● secretaire, secrétaire → secretaire
● vendeur, vendeuse → vendeur

Why stemming matters
● Return all possible relevant jobs given the
user’s query, not just exact matches

Stemming - Lucene Analyzers
● Do stemming before adding to inverted
index
● Examples
○ PorterStemFilter
○ SnowballAnalyzer
○ EnglishMinimalStemmer

Inverted Index
Job A: Directrice de Documentaires
Job B: Directeur de production
Token Job A Job B
de ✔ ✔
directeur ✔ ✔
documentaires ✔
production ✔

Search with stemming tokenizers
● At search time, use the same analyzer on
the query
○ “directrice” → “directeur”
● Search for “directrice” returns both jobs

Modifying stem rules require full
index rebuild
● If roots have changed need to re-
process all jobs

Token Job A Job B
de ✔ ✔
directeur ✔ ✔
documentaires ✔
production ✔

Drawbacks
● Loss of precise information
○ “Directrice” search should return exact match only

Decouple stemming
from indexing

Term Expansion Maps
● Map from String->List<String>
● Key is root, values are tokens that stem
to that root
● driver → driver, drivers
● vendeur → vendeur, vendeuse

Stemmer interface
● One method
● String stem(String token)
● Many implementations
● EnglishStemmer
● FrenchStemmer
● GermanStemmer
● SpanishStemmer

Building term expansion map
for each language
for each term in language
root = Stemmer.stem(term)
termMap[root].append(term)
● Takes ~1.5 minutes on index with 2
million tokens and 18 languages

Job A: Directrice de documentaires
Token Job A Job B
de ✔ ✔
directeur ✔ ✔
documentaires ✔
production ✔

Job A: Directrice de documentaires
Token Job A Job B
de ✔ ✔
directeur ✔
directrice ✔
documentaires ✔
production ✔

Search Service
“directrice”

“directrice”
“directeur”
French Stemmer

“directrice”
“directeur”
Term
Expansion
Map
French Stemmer
Query Rewriter

“directrice”
“directeur”
Term
Expansion
Map
French Stemmer
Query Rewriter
“directrice” OR “directeur”

Benefits
● Modifying stem rules don’t require index
rebuilds
○ Takes minutes on index with millions of jobs
○ Had flexibility to iteratively implement stemming
rules as we come across different use cases

Benefits
● Precise information
○ “directrice” search query returns exact match only

Code deploy to change
rules or add languages

49 team members
26 nationalities
18 languages

Scale Stemming
● Indeed continued international expansion
● Needed stemming to scale without code
deploys and coordination between
developers and country managers.

Goal
● Efficient
○ Store term expansion maps efficiently
○ Search time as fast as possible

Goal
● Generic
○ identify patterns common to all languages.
■ ies→y in English, se→r in French

Goal
● Comprehensive
○ Support all use cases we care about:
■ plurals
■ synonyms
■ abbreviations
■ accent collation
■ gender suffixes

Goal
● Scalable
○ Adding a new language shouldn’t need a code
deploy

Rule driven stemming
one stemmer. all languages.

What is a stemming rule
● Rules transform tokens into their root form

Rule attributes
● Rules have “from” (origin) and “to”
(replacement)

Rule attributes
● Rules have a type
○ Types define exactly how the text transformation
happens

Rule type - exact
● Change origin to replacement when its an
exact match

Exact rule
English
sr→senior
attorney→lawyer
Italian
colf→domestica
Dutch
leraar→docent

Rule type - substring
● Change all occurrences of origin to
replacement

Substring rule
English - é→e
résumé → resume
café → cafe
German - ä → a
verkäufer → verkaufer
French - ô→o
hôtesse → hotesse

Rule type - suffix
● Change origin to replacement if it matches
at the end of token

Suffix Rule - English
● ies→y
○ families → family
○ policies → policy
● s→’’
○ nurses → nurse
○ drivers → driver

Suffix Rule - French
● euse→eur
○ serveuse→serveur
● ienne→ien
○ gardienne→ gardien

Order matters
Stem “families”
Rules
● s→’’
● ies→y
Apply s→’’

Order matters
Stem “families”
Rules
● s→’’
● ies→y
Apply s→’’
● families → familie

Stem “families”
Rules
● s→’’
● ies→y
Apply s→’’
● families → familie
Order matters
✘

Rules can be marked as terminal
● No more rules applied after terminal rule

Prevent over-stemming
● s → ‘’ can cause this → thi
● Min Length - special terminal rule
● Usually set to anywhere from 3 to 5

Babelfish: Stem rule editor
● Webapp to edit and publish rules
● Rules interpreted by generic stemmer
● 27 languages

directrices
directrice suffix rule “s” → “”
directeur suffix rule “trice” → “teur”
ingénieur
ingenieur substring rule “é” → “e”

Job
Seekers
Stem Rule Editor
EN s → ‘’, ces → y, …
FR e → é, u → ù, …
Jobs Index Builder
Term Expansion Map
sale → sale, sales
policy → policy, policies
Search Service
Country Managers
query
results

Term expansion map storage
● Custom serialization format
○ Store string array as UTF8 bytes and offsets
○ Front encoding for additional compression
● 2X smaller than using Java native
serialization

Comprehensive
● Gender
● Accents
● Plurals
● Synonyms

Scalable
27 languages use stemming rules
Re-used language detection and stemming
libraries in resume search

Efficient
● Term expansion map in Europe index has 2
million terms in 18 languages - 60MB on
disk
● Building term expansion maps takes ~ 1.5
minutes
● Doing boolean query for stemming adds
~5ms to median search time (~35ms)

Stemming helps job seekers
Searches that return no jobs reduced by 60%
with stemming
3% to 5% more clicks

Sponsored Jobs at Indeed
Real-time auction used to determine
Sponsored Job impressions

Real-time auction used to determine
Sponsored Job impressions
Auction winner based on expected value

Expected
Value
= Bid x eCTR
Expected
Click-Through
Rate*

Job Bid
A $3.00
B $2.00
C $1.00

Job Bid eCTR
A $3.00 5%
B $2.00 10%
C $1.00 8%

Job Bid x eCTR = Value
A $3.00 5% $0.15
B $2.00 10% $0.20
C $1.00 8% $0.08

Job Bid x eCTR = Value → Rank
A $3.00 5% $0.15 2
B $2.00 10% $0.20 1
C $1.00 8% $0.08 3

B $2.00 10% $0.20 1
A $3.00 5% $0.15 2

B $2.00 10% $0.20 1
A $3.00 5% $0.15 2
B could win the auction with a lower bid...

…only charge what’s needed to win!
B $2.00 10% $0.20 1
A $3.00 5% $0.15 2

B $2.00 10% $0.20 1
A $3.00 5% $0.15 2
$1.50 x 10% = $0.15

Cost = $1.51
B $2.00 10% $0.20 1
A $3.00 5% $0.15 2

“Generalized Second Price Auction”

“Generalized Second Price Auction”
● Fair for employers
● Ensures sponsored results are relevant and
useful for job seekers

Employers set their bid & budget

Employers set their bid & budget
employer_id int(10) unsigned,
bid decimal(10,2) unsigned,
daily_budget decimal(10,2) unsigned,

A builder process creates read-optimized data
structures for the auction system

On search results page, execute auction to
determine sponsored impressions

When job seeker clicks on sponsored result,
log information from the auction
employerId
jobId
bid
cost
…

Process click logs to update budgets and
charge employers

Process click logs to update budgets and
charge employers
Apply business rules during click processing:
● Fraud detection
● Duplicate click detection

SJ outside the US
Non-US employers wanted their jobs in
sponsored results...

SJ outside the US
Non-US employers wanted their jobs in
sponsored results...
...but they don’t have US Dollars

SJ outside the US
v1: Use credit cards
Credit card company convert charges to
employer’s currency

SJ outside the US
Credit Cards
+ No changes needed

SJ outside the US
Credit Cards
+ No changes needed
- Bad UX for employers

SJ outside the US
Credit Cards
+ No changes needed
- Disadvantaged exchange rates

SJ outside the US
Credit Cards
+ No changes needed
- Disadvantaged exchange rates
- Employers bear currency risk

Credit Cards: Currency Risk
Desired Daily Budget: CA $100.00
Exchange rate on Jan 1: 0.9351
Set Daily Budget to: $93.51

Effective Daily Budget: CA $104.25

+4.25%
Effective Daily Budget: CA $104.25

Multi-currency
Sponsored Jobs
Auction

Multi-currency SJ
Employers can set bids and budgets in
preferred currency
Canadian Dollars CAD
Australian Dollars AUD
Japanese Yen JPY
Euro EUR
British Pounds GBP
Swiss Francs CHF

Multi-currency SJ
Single auction for all employers using any
currency

Multi-currency SJ
Fair exchange rates for employers

Multi-currency SJ
Transparent and repeatable calculations

Multi-currency SJ
Create a new “pseudo-currency” for use within
the auction:
millicent

Millicents
Exchange rate between USD and millicents
is fixed:
$0.01 == 1000 millicents
$1.00 == 105
millicents

Millicents
Exchange rates between other currencies and
millicents can vary over time:
€1.00 == 136,170 millicents
¥100 == 98,350 millicents

Millicents
Provide enough granularity to differentiate
similar values in different currencies

Millicents
All of these are about $1.00 (USD):
£0.60 (GBP)
€0.73 (EUR)
¥102 (JPY)

Millicents
All of these are about $1.00 (USD):
£0.60 (GBP)
€0.73 (EUR) Which is larger?
¥102 (JPY)

Millicents
Converting to USD doesn’t help
USD: $1.00 → $1.00
GBP: £0.60 → $1.00
EUR: €0.73 → $1.00
JPY: ¥102 → $1.00

Millicents
Millicents provide granularity to rank values
USD: $1.00 → 100000 mc
GBP: £0.60 → 100450 mc
EUR: €0.73 → 99519 mc
JPY: ¥102 → 100317 mc

Millicents
32 bit signed values
$21,474 USD equivalent
$9.2 trillion USD equivalent

Local Currency Values
Values in specific currency are represented
with currency code and an integer
Integer represents “minor unit”, depends on
the currency type:
(USD, 543) == $5.43
(EUR, 543) == €5.43
(JPY, 543) == ¥543

For each currency, preferable that the “minor
unit” is roughly equal to $0.01 USD
● Exchange rate representation
● Fairness in auction competition

$21 million USD (and others)
¥2.1 billion JPY
$90 quadrillion USD (and others)
¥9 quintillion JPY

Multi-currency SJ
Change bid and budget representations to use
[currency, integer]

Multi-currency SJ
Create process to retrieve and record
exchange rates every day

Multi-currency SJ
Auction builder process converts bids to
millicents, saves the exchange rate used

Multi-currency SJ
Execute auction in millicents

Multi-currency SJ
Record results in millicents & local currency

Multi-currency SJ
Add multi-currency data to click logs:
employerId
jobId
bid
cost
...
employerId
jobId
currency
exchangeRate
bidInCurrency
bidMillicents
costMillicents
...

Multi-currency SJ
During click processing, convert auction cost
(in millicents) back to employer’s currency
using same exchange rate
costInMillicents
currency
exchangeRate
→ costInCurrency

“How much revenue did we make today?”
$1,000

$1,000
$548 USD
€273 EUR
¥8,253 JPY

$1,000
$548 USD
€273 EUR
¥8,253 JPY
100,000,000 mc

Revenue Reporting
If the auction millicent cost is used, there could
be errors!

Revenue Reporting
be errors!
Millicent Cost: 53,826 millicents
Euro Cost: €0.39483

Revenue Reporting
be errors!
Euro Cost: €0.39
Actual Millicent Cost: 53,168 millicents

Revenue Reporting
be errors!
Euro Cost: €0.39
Actual Millicent Cost: 53,168 millicents
1.2% difference!

Active Non-US Employers
Great Britain
Japan
Canada

30% of Sponsored clicks non-USD

International Success
United Kingdom 1.) Indeed 2.) Reed 3.) Totaljobs
France 1.) Indeed 2.) Cadremploi 3.) Monster
Netherlands 1.) Indeed 2.) NVB 3.) Monsterboard
Canda 1.) Indeed 2.) Workopolis 3.) Monster
Italy 1.) Indeed 2.) Infojobs 3.) Jobrapido
Brazil 1.) Indeed 2.) Catho 3.) Infojobs
Japan 1.) Rikunabi 2.) Indeed 3.) Rikunabi Next
Australia 1.) Seek 2.) Indeed 3.) Careerone
India 1.) Naukri 2.) Timesjobs 3.) Indeed

Next @IndeedEng Talk
August 27th, 2014
http://engineering.indeed.com/talks
https://twitter.com/IndeedEng

@IndeedEng: Tokens and Millicents - technical challenges in launching Indeed around the world

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