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Mukund
Narasimhan
May 19, 2017
MLConf2017
KnowledgeatPinterest
Helppeoplediscover
anddothethingsthey
love.
Goal
Helppeoplediscover
anddothethingsthey
love.
Helpusersdiscover
personalizednewideas.
Goal
400+Engineers
2B+Boards
100B+Pins
175M+Monthly active users
Avisualbookmarksomeone
hassavedfromtheinternetto
aboardthey’vecreated.
Pin
6
men’s style
Jacob Hinmon
End Clothing
Men’s blue jacket
Pinsare
notphotos
Under the hood
Pin
Image
Agreatercollection

ofideas.
Board
8
MMonochromaticNormcoreFashion
48 Pins
A house
Trying not to die in the
cold up in the northwest.
Visit anyways.
Andreas
Pihlström
Problem
Helpuserdiscoverpersonalizednewideas
Howcanwematchideastousers?
Solution1:Explicitlyfollowedlistoftopics
• Returnideasbasedonexplicituserinterest(homedecor,fashionetc.)
• Userprovidesanexplicitlistofinterests
• Modelmapsuserstoahard-codedlistofinterests
• Discoverinterestsfromdata,andmapuserstothoseinterests
Solution2:Inferreduserinterests
• Returnideasbasedoninferreduserinterests(noexplicittopics)
Bothareimportant!
• Wewilldiscussboth,butwewillgointotechnicaldetailsforsolution2only.
ExplicitTopics
Domain
ExplicitTopics
Sub-Domain
Resources
• UserCuration: Pinsareorganizedintoboards
• UserActivity:usersinteractwithPinsandboards
• Content: Pinshaveassociatedcontent
Whatdataisavailabletous?
Curation
Pin-Board-Usergraph
•Canbeusedtodiscovertopics
•Canalsobeusedasasignalforthe inferredinterestsmodel
Content
•Canbeusedtodiscover
topics
•Canalsobeusedasasignal
forthe inferredinterestsmodel
PinDescription
Boardtitle
Url
Webpage
LogAnalysis
Activity
•Usersearchforpins,save
pinstoboards,etc.
•Canbeusedtotrainatopics
model
•Canbeusedtotrain an
inferredinterestsmodel
•Canbeusedtomodelusers
InferredUserInterests
Goal:returnPins
mostrelevanttouser
Idea
• Trainembeddingsforusers
(=query)andPins(=result)insame
embeddingsspace
• Atruntime,findthepinembeddings
thatbestmatchtheuser
embeddingusinganinvertedindex.
Returnthetoppinstotheuser
InferredUserInterests
Twokeytechnology
components
• Embeddings
• InvertedIndex
InferredUserInterests
Embeddings
• Dense,lower-dimensional,continuous
representationofobjects
• Representationismeaningful,captures
semanticsimilarity.Twoobjectsthataresimilar
intheembeddingsspaceshouldbe
semanticallysimilar.
InferredUserInterests
InvertedIndex
• Givenanembedding,findobjectsthataresimilar
intheembeddingsspace
• Problem:toocostlytosearchthroughallpotential
candidates
• Solution:useLocalitySensitiveHashing(LSH)
• Next:Howcanweindexembeddings?
HowareEmbeddingsCreated?
Severaltechniquesavailable-herewecoverWord2Vec
• Word2Vecencodeswordsimilaritybasedonthedistributionalhypothesis(Harris,1954)–words
insimilarcontexthavesimilarmeanings
• TotrainembeddingswithWord2Vec,definecontainer(sentence),item(word),context
(surroundingwords),
• Here,container=Pin,item=content,context=othercontent
where content = Pin description, title of boards contained with the Pin, etc.
Original Word2Vec Inferred User Interests
Container Sentence Pin
Item Word Content: word in content associated with Pin
Context Surrounding words Other content: other words in content associated with Pin
HowareEmbeddingsTrained?
NeuralNetworkModel
• StartwiththevocabularyVofwords.
• Thetrainingdataconsistsofinstances(v,C(v))
• V=term[word1]
• C(v)=contextfortheterm[wordsco-occurringwithword1]
• Defineascoringfunction
• Trainingconsistsofminimizing
• TheminimizerEistheembeddingmatrix.
• Theembeddingsmatrixmapsv[word]->embedding
InvertedIndex
UseLocalitySensitiveHashingtocreate
indexableterms
• Reminder:givenanembedding,wewanttofindobjectsthataresimilarinthe
embeddingsspace
• Thisproperty,wherecloserobjects(intermsofcosinesimilarity)aremorelikely
tohavethesamevalueforthebucketcorrespondingtothesetofallmbitsis
calledLocalitySensitiveHashing.
• Idea:convertembeddingsintoindexableterms
• Converttherealvaluedembeddingvectorintoabinaryvector1101011101110…
• Theprobabilityoftwovectorshavingthesamevalueofbitiisproportionalto
thecosinesimilaritybetweenthetwovectors
LocalitySensitiveHashing
Pickprojectionvectorsonce
LocalitySensitiveHashing
Foreachembeddingsvector
LocalitySensitiveHashing
Foreachprojectionvector,
determineonwhichsideofthe
hyperplanetheembeddings
vectorlands
• On the same side: set bit to 1
• On different sides: set bit to 0
Result 1: 110
11
1
0
LocalitySensitiveHashing
Dothesamewiththe
nextembeddings
vector
Result 1: 110
Result 2: 101
1
1
0
1
0
1
LocalitySensitiveHashing
Thetwoembeddingsare
farinboththeembeddings
spaceandinthebitspace
Embeddings: (-1.0, 1.2), (1.3, -0.7)
Cosine similarity: 0.05
Bits: 110, 101
1 out of 3 bits matches
1
1
0
1
0
1
CreateandIndexTerms
Createterms
Pickoptimalnumberofterms andbitsperterm
Wewanttobeopentodiversecandidates:
• Setalowthresholdforthenumberoftermsthatmustmatch(atmostxtermsmustmatch)
• Createalargenumberofsmallerterms(notethateachtermmustmatchexactly)
Butwewanttomaintainacertainlevelofprecision:
• Setaminimumthreshold(atleastxtermsmustmatch)
• Setaminimumsizeforterms
Example1001110001011000->1001-1100-0101-1000
Indexterms
RetrieveresultswithaWAND(WeakAnd)query
InferredUserInterests
Goal:returnPins
mostrelevanttouser
Idea
• Trainembeddingsforusers
(=query)andPins(=result)insame
embeddingsspace
• Atruntime,findthepinembeddings
thatbestmatchtheuser
embeddingusinganinvertedindex.
Returnthetoppinstotheuser
InferredUserinterests
Pinsare
independentof
popularitysomay
berecentorrarely
seen
Wasuploaded12daysago
andhas580impressions
Wasuploaded38daysago
andhasonly140impressions
Hasonly100impressions
InferredUserinterests
ApproximateNearest
Neighborsearchusing
invertedIndexapplied
toembeddings
• Thisismypersonalfeedwith
resultsthatmatchmyinterests,e.g.
animals,castles,howtodrawwith
colorpencils,gardening
• Someofthesewereidentifiedusing
themethoddescribedinthistalk
We’re hiring !
sonjaknoll@pinterest.com
Thankyou!
© Copyright, All Rights Reserved, Pinterest Inc. 2017

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