Enhancing relevancy through personalization & semantic search

Dublin, IE
2013.11.07

Trey Grainger

ENHANCING RELEVANCY THROUGH
PERSONALIZATION & SEMANTIC SEARCH

Search Technology Development Manager

@"

My Background

Trey"Grainger"

Search"Technology"Development"Manager"
""@CareerBuilder.com"
"
Relevant"Background"
•  Search"&"Recommenda>ons"
•  HighAvolume,"Distributed"Systems"
•  NLP,"Relevancy"Tuning,"User"Group"Tes>ng,"&"Machine"Learning"
"

"""""""""""""""""""""""""""Other"Projects"

•  CoAauthor:""Solr%in%Ac*on%
•  Founder"and"Chief"Engineer"@"""""""""""""""""""""""""".com"

Roadmap
• 
• 
• 

I. How we use Solr @ CareerBuilder
II. Traditional Relevancy Scoring
III. Advanced Relevancy through functions
–  Factors as a linear function
–  Context-aware relevancy parameter weighting

• 

III. Personalization & Recommendations
–  Profile and Behavior-based
–  Solr as a recommendation engine
–  Collaborative Filtering

• 

IV. Semantic Search
– 
– 
– 
– 
– 

Mining user-behavior for synonyms
Uncovering meaning through clustering
Latent Semantic Indexing overview
Document-based searching
Foreground vs. Background analysis

How"we"use"Solr"@"CareerBuilder"

Search Scale @

• 
• 
• 
• 
• 
• 

Over"2.5"million"new"jobs"each"month""
Over"60"million"ac>vely"searchable"resumes"
~300"globally"distributed"search"servers""
Thousands"of"unique,"dynamically"generated"indexes"
Over"1"Billion"ac>vely"searchable"documents"
Over"1"million"searches"an"hour"

Data Analytics (market supply)

Data Analytics (market demand)

Data Analytics (labor pressure: supply/demand)

Data Analytics (hiring comparison per market)

Default Lucene Relevancy Algorithm (DefaultSimilarity)
Score(q,d)"="""
""""""∑""("-(t"in"d)".""idf(t)2"."t.getBoost()"."norm(t,"d)")6.6coord(q,"d)".6queryNorm(q)
"""""t"in"q"

""""

"
Where:""
"t"="term;"d"="document;"q"="query;"f"="ﬁeld"
666666666-(t"in"d)""=""numTermOccurrencesInDocument"½"
666666666idf(t)"=""1"+"log"(numDocs"/"(docFreq"+"1))"
666666666coord(q,"d)"="numTermsInDocumentFromQuery"/"numTermsInQuery"
666666666queryNorm(q)"="1"/"(sumOfSquaredWeights"½")"
666666666sumOfSquaredWeights"="q.getBoost()2"."∑"("idf(t)"."t.getBoost()")2""
"""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""t"in"q"

666666666norm(t,"d)"""="""d.getBoost()""f""lengthNorm(f)""f"""f.getBoost()"
*Source:"Solr%in%Ac*on,"chapter"3"

6

TF * IDF
• 

Term Frequency: “How well a term describes a document?”
–  Measure: how often a term occurs per document

• 

Inverse Document Frequency: “How important is a term overall?”
–  Measure: how rare the term is across all documents

Boosting documents and fields

• 

Certain fields may be more important than other fields:
–  The Job Title and Skills may be more relevant than other aspects of the job:
/select?qf=jobtitle^10 skills^5 jobrequirements^2 jobdescription^1

• 

It’s possible to boost documents and fields at both index time and query time

• 

If you need more fine-grained control (such as per-term index-time boosting),
you can make use of payloads

Custom scoring with Payloads
• 

In addition to boosting search terms and fields, content within Fields can also be
boosted differently using Payloads (requires a custom scoring implementation):
design [1] / engineer [1] / really [ ] / great [ ] / job [ ] / ten[3] / years[3] /
experience[3] / careerbuilder [2] / design [2], …
jobtitle: bucket=[1] boost=10; company: bucket=[2] boost=4;
jobdescription: bucket=[ ] weight=1; experience: bucket=[3] weight=1.5
We can pass in a parameter to solr at query time specifying the boost to apply to each
bucket i.e. …&bucketWeights=1:10;2:4;3:1.5;default:1;

• 

This allows us to map many relevancy buckets to search terms at index time and adjust
the weighting at query time without having to search across hundreds of fields.

• 

By making all scoring parameters overridable at query time, we are able to do A / B
testing to consistently improve our relevancy model

That’s great, but what about domain-specific knowledge?
• 
• 
• 
• 
• 

News search: popularity and freshness drive relevance
Restaurant search: geographical proximity and price range are critical
Ecommerce: likelihood of a purchase is key
Movie search: More popular titles are generally more relevant
Job search: category of job, salary range, and geographical proximity matter

TF * IDF of keywords can’t hold it’s own against good
domain-specific relevance factors!

Advanced"Relevancy"through"Func>ons"

Example of domain-specific relevancy calculation
News website:
/select?
fq=$myQuery&
25%"
q=_query_:"{!func}scale(query($myQuery),0,100)"
AND _query_:"{!func}div(100,map(geodist(),0,1,1))"
25%"
AND _query_:"{!func}recip(rord(publicationDate),0,100,100)"
25%"
AND _query_:"{!func}scale(popularity,0,100)"&
myQuery="street festival"&
25%"
sfield=location&
pt=33.748,-84.391

*Example"from"chapter"16"of"Solr%in%Ac*on%

Fancy boosting functions
• 

Separating “relevancy” and “filtering” from the query:
q=_val_:"$keywords"&fq={!cache=false v=$keywords}&keywords=solr

• 

Keywords (50%) + distance (25%) + category (25%)
q=_val_:"scale(mul(query($keywords),1),0,50)" AND
_val_:"scale(sum($radiusInKm,mul(query($distance),-1)),0,25)” AND
_val_:"scale(mul(query($category),1),0,25)"
&keywords=solr
&radiusInKm=48.28
&distance=_val_:"geodist(latitudelongitude.latlon_is,33.77402,-84.29659)”
&category=jobtitle:"java developer"
&fq={!cache=false v=$keywords}

Context aware relevancy
Example: Willingness to relocate for a job
2,500"
2,000"
1,500"
1,000"
500"
0"

So>ware6engineers6
Food6service6workers6
1%" 5%" 10%" 20%" 25%" 30%" 40%" 50%" 60%" 70%" 75%" 80%" 90%" 95%"

Willingness to relocate

Somware"engineers"in"Chicago"want"jobs"in"these"loca>ons:"

Willingness to relocate

Food"service"workers"in"Chicago"want"jobs"in"these"loca>ons:"

Personaliza>on"&"Recommenda>ons"

Beyond domain knowledge… consider per-user knowledge
• 

John lives in Boston but wants to move to New York or possibly another big city.
He is currently a sales manager but wants to move towards business
development.

• 

Irene is a bartender in Dublin and is only interested in jobs within 10KM of her
location in the food service industry.

• 

Irfan is a software engineer in Atlanta and is interested in software engineering
jobs at a Big Data company. He is happy to move across the U.S. for the right job.

• 

Jane is a nurse educator in Boston seeking between $40K and $60K working in
the healthcare industry

Query for Jane
Jane is a nurse educator in Boston seeking between $40K and $60K
working in the healthcare industry

http://localhost:8983/solr/jobs/select/?
fl=jobtitle,city,state,salary&
q=(
jobtitle:"nurse educator"^25 OR jobtitle:(nurse educator)^10
)
AND (
(city:"Boston" AND state:"MA")^15
OR state:"MA”)
AND _val_:"map(salary, 40000, 60000,10, 0)”

*Example from chapter 16 of Solr in Action

Search Results for Jane
{ ...
"response":{"numFound":22,"start":0,"docs":[
{"jobtitle":"Clinical Educator
(New England/ Boston)",
"city":"Boston",
"state":"MA",
"salary":41503},

{"jobtitle":"Nurse Educator",
"city":"Braintree",
"state":"MA",
"salary":56183},

{"jobtitle":"Nurse Educator",
"city":"Brighton",
"state":"MA",
"salary":71359}

…]}}

*Example documents available @ http://github.com/treygrainger/solr-in-action/

"

What did we just do?
• 

We built a recommendation engine!

• 

What is a recommendation engine?
–  A system that uses known information (or derived information from that
known information) to automatically suggest relevant content

• 

Our example was just an attribute based recommendation… we’ll see that
behavioral-based (i.e. collaborative filtering) is also possible.

Redefining “Search Engine”

•  “Lucene is a high-performance, full-featured
text search engine library…”
Yes,6but6really…6
•  "Lucene"is"a"highAperformance,"fullyAfeatured"
token"matching"and"scoring"library…"which"
can"perform"fullAtext"searching."

Redefining “Search Engine”

or,6in6machine6learning6speak:6
•  A"Lucene"index"is"mul>Adimensional""
sparse"matrix…"with"very"fast"and"powerful"lookup"
capabili>es."
•  Think"of"each"ﬁeld"as"a"matrix"containing"each"term"
mapped"to"each"document"

The Lucene Inverted Index (traditional text example)

What6you6SEND6to6Lucene/Solr:6

How6the6content6is6INDEXED6into6
Lucene/Solr6(conceptually):6

Document6

Content6Field6

Term6

Documents6

doc1""

once"upon"a">me,"in"a"land"far,"far"
away"

a"

doc1"[2x]"

brown"

doc2"

the"cow"jumped"over"the"moon."

doc3"[1x]","doc5"[1x]"

cat"

doc4"[1x]"

doc3""

the"quick"brown"fox"jumped"over"
the"lazy"dog."

cow"

doc2"[1x]","doc5"[1x]"

…"

...6

doc4"

the"cat"in"the"hat"

once"

doc1"[1x],"doc5"[1x]"

doc5"

The"brown"cow"said"“moo”"once."

over"

doc2"[1x],"doc3"[1x]"

the"

…"

…"

doc2"[2x],"doc3"[2x],"
doc4[2x],"doc5"[1x]"

…"

…"

Matching text queries to text fields

/solr/select/?q=jobcontent:“software engineer”
Job6Content6Field6

Documents6

…"

…"

engineer"

doc1,"doc3,"doc4,"doc5"

engineer"

doc5"

somware"engineer"

…"
mechanical"

doc2,"doc4,"doc6"

…"

…6

somware"

doc1,"doc3,"doc4,"doc7,"
doc8"

…"

…"

doc1"""""doc3""""
"""""""doc4"

somware"
doc7"""""doc8"

Beyond Text Searching

•  Lucene/Solr"is"a"search"matching"engine"
•  When"Lucene/Solr"search"text,"they"are"matching"
tokens"in"the"query"with"tokens"in"index"
•  Anything"that"can"be"searched"upon"can"form"the"
basis"of"matching"and"scoring:"
–  text,"atributes,"loca>ons,"results"of"func>ons,"user"
behavior,"classiﬁca>ons,"etc.""

Approaches to Recommendations
• 

Content-based
–  Attribute based
i.e. income level, hobbies, location, experience
–  Hierarchical
i.e. “medical//nursing//oncology”, “animal//dog//terrier”
–  Textual Similarity
i.e. Solr’s MoreLikeThis Request Handler & Search Handler
–  Concept Based
i.e. Solr => “software engineer”, “java”, “search”, “open source”

• 

Collaborative Filtering
“Users who liked that also liked this…”

• 

Hybrid Approaches

Collaborative Filtering
What6you6SEND6to6Lucene/Solr:6
Document6

“Users6who6bought6this6product”6ﬁeld6

doc1""

How6the6content6is6INDEXED6into6
Lucene/Solr6(conceptually):6
Term6

Documents6

user1,"user4,"user5"

user1"

doc1,"doc5"

doc2"

user2,"user3"

user2"

doc2"

doc3""

user4"

user3"

doc2"

doc4"

user4,"user5"

user4"

doc5"

user4,"user1"

doc1,"doc3,""
doc4,"doc5"

…"

…"

user5"

doc1,"doc46

…"

…"

Step 1: Find similar users who like the same documents

q=documen>d:"("doc1""OR""doc4")"
Document6

“Users6who6bought6this6product”6ﬁeld6

doc1""

user1,"user4,"user5"

doc2"

user2,"user3"

doc3""

user4"

doc4"

user4,"user5"

doc5"

user4,"user1"

…"

…"


doc16
user166666user466
6666
666666666user56

doc46
666user466666user56

TopAscoring"results"(most"similar"users):"
1)  "user4"(2"shared"likes)"
3)  "user"1"(1"shared"like)"

"

Step 2: Search for docs “liked” by those similar users

"""
Most"similar"users:"
"""""""""""""""""""""""""""""""""""""""""""""""""""""""/solr/select/?q=userlikes:("user4"^2"" "
"
" """""""""""""""""""""""""""""""""""""""""""""""""""""""""OR""user5"^2"OR""user1"^1)"
3)  "user"1"(1"shared"like)"
Term6

Documents6

user1"

doc1,"doc5"

user2"

doc2"

user3"

doc2"

user4"

doc1,"doc3,""
doc4,"doc5"

user5"

doc1,"doc46

…"

…"


Top"recommended"documents:"
1)"doc1"(matches"user4,"user5,"user1)"
2)"doc4"(matches"user4,"user5)"
3)"doc5"(matches"user4,"user1)"
4)"doc3"(matches"user4)"
"
//"doc2"does"not"match"

Building up to personalization
• 

Use what you have:
–  User’s keywords, IP address, searches, clicks, “likes” (purchases,
job applications, comments, etc.)
–  Build up a dossier of information on your users
–  If a user gives you a profile (resume, social profile, etc), even better.

For full coverage of building a recommendation engine in Solr…
• 

See my talk from Lucene Revolution 2012 (Boston):

Personalized Search
• 

Why limit yourself to JUST explicit search or JUST automated recommendations?

• 

By augmenting your user’s explicit queries with information you know about them, you
can personalize their search results.

• 

Examples:
–  A known software engineer runs a blank job search in New York…
•  Why not show software engineering higher in the results?
–  A new user runs a keyword-only search for nurse
•  Why not use the user’s IP address to boost documents geographically closer?

Not going to talk about…
•  Using the SynonymFilter
•  Automatic language detection
•  Stemming/lemmatization/multi-lingual search
•  Stopwords
(For all of the above, see the Solr Wiki, Reference Guide, or read Solr in Action)
• 

Instead, we’re going to cover:
–  Mining user behavior to discover synonyms/related queries
–  Discovering related concepts using document clustering in Solr
–  Future work: Latent Semantic Indexing
–  Document to Document searching using More Like This
–  Foreground/Background corpus analysis

Automatic Synonym Discovery
• 
• 

Our primary approach: Search Co-occurrences
Strategy: Map/Reduce job which computes similar searches run for the same
users
John searched for “java developer” and “j2ee”
Jane searched for “registered nurse” and “r.n.” and “prn”.
Zeke searched for “java developer” and “scala” and “jvm”

• 

By mining the searches of tens millions of search terms per day, we get a list of top
searches, with the corresponding top co-occurring searches.

• 

We also tie each search term to the top category of jobs (i.e java developer, truck
driver, etc.), so that we know in what context people search for each term.

Example of “related search terms”

Example:"“RN”:"
registered"nurse"6588,"
rn"registered"nurse"4300,"
nurse"2492,"
nursing"912,"
lpn"707,"
healthcare"453,"
rn"case"manager"446,"
registered"nurse"rn"404,"
director"of"nursing"321,"
case"manager"292"

Example:"“accoun>ng”"
accountant"8880,"
accounts"payable"5235,"
ﬁnance"3675,"
accoun>ng"clerk"3651,"
bookkeeper"3225,"
controller"2898,"
staﬀ"accountant"2866,"
accounts"receivable"2842"

Future work on building conceptual links
Latent Semantic Indexing
•  Concept: Build a matrix of all terms, perform singular value decomposition on that
Matrix to reduce the number of dimensions, and index the meaningful (i.e. blurred)
terms on each document.
• 

Why this matters: if done correctly, the search engine can automatically collapse
terms by meaning, remove the useless and redundant ones, and for it’s own
conceptual model of your domain space. This can be used to infuse more
meaning into a document than just a keyword.

• 

See blog posts and presentations by John Berryman and Doug Turnbull about
their work on this. They’re leading the way on this right now (in the open-source
community).

• 

http://www.opensourceconnections.com/2013/08/25/semantic-search-with-solr-and-python-numpy

Using Clustering to find semantic links

Setting up Clustering in solrconfig.xml
<searchComponent.name="clustering".enable=“true“..class="solr.clustering.ClusteringComponent">"
..<lst.name="engine">"
....<str.name="name">default</str>"
....<str.name="carrot.algorithm">.
.org.carrot2.clustering.lingo.LingoClusteringAlgorithm</str>"
....<str.name="MultilingualClustering.defaultLanguage">ENGLISH</str>"
..</lst>"
</searchComponent>"
."
<requestHandler.name="/clustering".enable=“true".class="solr.SearchHandler">"
..<lst.name="defaults">"
....<str.name="clustering.engine">default</str>"
....<bool.name="clustering.results">true</bool>"
....<str.name="fl">*,score</str>"
..</lst>"
..<arr.name="lastIcomponents">"
....<str>clustering</str>"
..</arr>"
</requestHandler>"

Clustering Query

/solr/clustering/?q=(solr or lucene)
&rows=100
&carrot.title=titlefield
&carrot.snippet=titlefield
&LingoClusteringAlgorithm.desiredClusterCountBase=25
//clustering & grouping don’t currently play nicely
Allows you to dynamically identify “concepts” and their
prevalence within a user’s top search results

Clustering Results

Stage"1:"Iden>fy"Concepts"
Original"Query:"""q=(solr"or"lucene)""""
"
"
"
"
"
"
"//"can"be"a"user’s"search,"their"job">tle,""a"list"of"skills,"
//"or"any"other"keyword"rich"data"source"

Clusters Identified:

"

Developer (22)
Java Developer (13)
Software (10)
Senior Java Developer (9)
Architect (6)
Software Engineer (6)
Web Developer (5)
Search (3)
"
"
""""""""""""""""""""
Software Developer (3)
Systems (3)
Administrator (2)
Hadoop Engineer (2)
Java J2EE (2)
Search Development (2)
Software Architect (2)
Solutions Architect (2)

Stage"2:"Use"Seman>c"Links"in"your"relevancy"calcula>on"
q=content:(“Developer”^22"or"“Java"Developer”^13"or"“Somware"
”^10"or"“Senior"Java"Developer”^9""or"“Architect"”^6"or"“Somware"
Engineer”^6"or"“Web"Developer"”^5"or"“Search”^3"or"“Somware"
Developer”^3"or"“Systems”^3"or"“Administrator”^2"or"“Hadoop"
Engineer”^2"or"“Java"J2EE”^2"or"“Search"Development”^2"or"
“Somware"Architect”^2"or"“Solu>ons"Architect”^2)6
6
//6Your6can6also6add6the6user’s6loca[on6or6the6original6keywords6to6the66
//6recommenda[ons6search6if6it6helps6results6quality6for6your6usecase."

Document to Document Searching

Goal: use an entire document as your Solr Query, recommending
other related documents.
Standard approach: More Like This Handler
Alternative Approach: Foreground vs. Background corpus analysis

More Like This (Query)
solrconfig.xml:
<requestHandler name="/mlt" class="solr.MoreLikeThisHandler" />
Query:
/solr/jobs/mlt/?df=jobdescription&
fl=id,jobtitle&
rows=3&
q=J2EE&
// recommendations based on top scoring doc
mlt.fl=jobtitle,jobdescription& // inspect these fields for interesting terms
mlt.interestingTerms=details& // return the interesting terms
mlt.boost=true

*Example"from"chapter"16"of"Solr%in%Ac*on%

More Like This (Results)
{"match":{"numFound":122,"start":0,"docs":[
{"id":"fc57931d42a7ccce3552c04f3db40af8dabc99dc",
"jobtitle":"Senior

Java / J2EE Developer"}]

},
"response":{"numFound":2225,"start":0,"docs":[
{"id":"0e953179408d710679e5ddbd15ab0dfae52ffa6c",
"jobtitle":"Sr

Core Java Developer"},

{"id":"5ce796c758ee30ed1b3da1fc52b0595c023de2db",
"jobtitle":"Applications

Developer"},

{"id":"1e46dd6be1750fc50c18578b7791ad2378b90bdd",
"jobtitle":"Java Architect/

Lead Java Developer WJAV Java - Java in Pittsburgh PA"},]},

""interes>ngTerms":[" "

"
""""""""
""""""jobdescrip>on:j2ee",1.0,"
""""""jobdescrip>on:java",0.68131137,"
""""""jobdescrip>on:senior",0.52161527,"
""""""job>tle:developer",0.44706684,"
""""""jobdescrip>on:source",0.2417754,"
""""""jobdescrip>on:code",0.17976432,"
""""""jobdescrip>on:is",0.17765637,"
""""""jobdescrip>on:client",0.17331646,"
""""""jobdescrip>on:our",0.11985878,"
""""""jobdescrip>on:for",0.07928475,"
""""""jobdescrip>on:a",0.07875194,"
""""""jobdescrip>on:to",0.07741922,"
""""""jobdescrip>on:and",0.07479082]}}"

More Like This (passing in external document)

/solr/jobs/mlt/? df=jobdescription&
fl=id,jobtitle&
mlt.fl=jobtitle,jobdescription&
mlt.interestingTerms=details&
mlt.boost=true
stream.body=Solr is an open source enterprise search platform from the Apache
Lucene project. Its major features include full-text search, hit highlighting, faceted search,
dynamic clustering, database integration, and rich document (e.g., Word, PDF) handling.
Providing distributed search and index replication, Solr is highly scalable. Solr is the most
popular enterprise search engine. Solr 4 adds NoSQL features.

More Like This (Results)
{"response":{"numFound":2221,"start":0,"docs":[
{"id":"eff5ac098d056a7ea6b1306986c3ae511f2d0d89 ",
• 

"jobtitle":"Enterprise

Search Architect…"},

{"id":"37abb52b6fe63d601e5457641d2cf5ae83fdc799 ",
"jobtitle":"Sr.

Java Developer"},

{"id":"349091293478dfd3319472e920cf65657276bda4 ",
"jobtitle":"Java

Lucene Software Engineer"},]},

""interes>ngTerms":["
""""""jobdescrip>on:search",1.0,"
""""""jobdescrip>on:solr",0.9155779,"
""""""jobdescrip>on:features",0.36472517,"
""""""jobdescrip>on:enterprise",0.30173126,"
""""""jobdescrip>on:is",0.17626463,"
""""""jobdescrip>on:the",0.102924034,"
""""""jobdescrip>on:and",0.098939896]}"}"

CareerBuilder’s Alternative approach (“enhanced” More Like This)
I. Send document as content stream to Solr
II. Perform Language Identification on the content
III. Do language-specific parts of speech detection
•  Keep nouns, remove other parts of speech (removes noise)
IV. Do analysis of additional terms for statistical significance:
tf * idf OR foreground vs. background corpus comparison OR Both
Preferred statistical significance measure:
countFG(x) - totalCountFG * probBG(x)

z=

-------------------------------------------------------sqrt(totalCountFG * probBG(x) * (1 - probBG(x)))

V. Return top scoring terms

Foreground vs. Background Corpus Comparison
/solr/doc2doc?
fg=category:"software engineer"&bg=*:*&stream.body=java nurse and is are was
were ruby php solr oncology part-time … other text in a really long document”
Terms statistically more likely to appear in foreground query than background query:
java
ruby
We"are"essen>ally"boos>ng"terms"which"are"more"related"to"
some"known"feature"(and"ignoring"terms"which"are"equally"
php
likely"to"appear"in"the"background"corpus)"
document
Note: This method requires you pre-classify your documents (which we do)… it
doesn’t work with a document that hasn’t already been classified.

Pulling it all together

Tradi>onal"
Search"

Personalized"
Search"
Proﬁt!"

Seman>c"
Search"

Recommenda>ons"

Take-aways
• 

Lucene’s inverted index is a sparse matrix useful for traditional search
(keywords, locations, etc.), recommendations, and discovering links
between terms/tokens

• 

Traditional tf * idf keyword search is a good starting point, but the best
relevancy lies in combining your domain knowledge (knowledge of user’s
in aggregate) and user-specific knowledge into your own relevancy
factors.

• 

The ability to understand user queries (semantic search) further
enhances the search experience, and you already have many tools at
your fingertips for this.

Questions?

!  Trey6Grainger6
trey.grainger@careerbuilder.com6
@treygrainger6
6
6
6
6
Other6presenta[ons:666
66666h_p://www.treygrainger.com6

htp://solrinac>on.com"

Enhancing relevancy through personalization & semantic search

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