The paper discusses the challenges and research opportunities in e-commerce search and recommendations, specifically focusing on matching and ranking, conversational search, and fairness in the customer experience. It highlights the importance of customer and business goals, evolving queries, and the complex nature of e-commerce inventories, while emphasizing the necessity for effective data logistics and adaptive ranking techniques. Furthermore, practical limitations of learning to rank in e-commerce systems and their implications on user satisfaction and business success are explored.

1. Introducing"Challengesandresearchopportunitiesin
eCommercesearchandrecommendations"
Speaker:@hurutoriya
Date:2021-07-07
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2. What&Whythispaper?
ThispaperisSIGIRForumArticle.AuthorsareorganizersofSIGIReCom.Itwell
summalizedresearchhistoryateCommercesearch&recommendationdomain.
SIGIReComwillbringtogetherpractitionersandresearchersfromacademiaand
industrytodiscussthechallengesandapproachestoproductsearchand
recommendationineCommerce.
PaperlinkinAmazonScience
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3. AspectsofeCommercesearchanddiscovery
1.Customergoal
2.Businessgoal
3.Datalogistics
ThreeeCommerceresearchareas
1.Matchingandranking
2.Coversationalsearch
3.Fairness,confidentialityandtransparency
Wefocuson Matching and ranking inthistalk.
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4. Uniquepointsofproductsearch
Productsearchhastwomainstakeholderswhoseinterestscoopratebutalsocompete.
1.Customers
Cooperation :Needwhatbusinessesoffer
Compete :Wanttofindthebestqualityatthecheapestprice
2.Businessowners
Cooperation :Needcistomerpurchasestosurvive
Compete :Wanttomaximizeprofit
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5. Customers
CustomersvisiteCommercesitestoaccomplishagoal.
Goals
1. simple :e.g.buyingacoffeemachine
2. complex e.g.fixingaholeinthewall
saerchqueriesandinteractions→Customerintents→CustomerJourneys
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6. Queryintent
websearchqueriesintent
navigational,informational,transactional
eCommercesearchqueriesintent
UserIntent,Behaviour,andPerceivedSatisfactioninProductSearch,WSDM2018
targetfinding,decisionmaking,andexploration.
ATaxonomyofQueriesforE-commerceSearch,SIGIR2018bywalmart
shallowexploration,targetedpurchase,major-itemshopping,minor-item
shopping,andhard-choiceshopping
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7. On-sitecustomerjourney
Customersjourneyviafunnel
i.broadqueries
ii.refinementsqueries
iii.examiningmultipleproductsbeforedecisionmaking
ReturnstoConsumerSearch:EvidencefromeBay,EconomicsandComputation
2016
LargeportionofeCommercecustomerjourneysareinitiallyexploratory,
recommendationsarevaluable.
Searchbecomesmoreimportantoncethecustomerhasshapedtheirviewofwhat
theywant.
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8. Globalcustomerjourney
Thecustomerjourneycanspanmultiplesitesandofflineinteractions
Proposeasubstituteproductsystemtoavoidazerohitresult
i.Accesstoknowledgeoutsideofwhatisavailableinthecatalog
ii.Accesstotheglobalstateofcustomerjourney
LeadingConversationalSearchbySuggestingUsefulQuestions,WWW2020
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9. Business
Customersatisfactionisimportantforbusiness,butisonlyoneofthemanycriteriathata
businessneedstotracktowardsthegoalofoptimizingprofit
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10. Salesstrategiesandshort-andlong-termeffects
cross-selling :Enticingcustomerstobuyadditionalproducts
up-selling :Temptingcustomerstobuyamoreprofitableversionofaproduct
down-selling :Encouragingcustomerstobuybymatchingtheirbudget
e.g.BusinessPushthedown-selltoselltheitemswhichislower-qualityandcheaper.
shortterm:earntheprofit
longterm:mayycustomersnottoreturninthefuture.
cross-sellapproach
backfilltheSERPwithrecommendationsresultthatrelatedtosaerchresult.
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11. Brandimageandinventory.
e.g.exampleofAmazon
AninterestingchallengeintheFashionStoreisthediscrepancybetweenwhatthe
majorityofcustomersactuallybuyandwhattheywanttoseeontopofthepage.The
itemmostcommonlyboughtforthequery”diamondring”mightbeacheap
zirconiumring.However,ifweshowthezirconiumringasafirstresult,oursearchwill
beperceivedasbroken.Besides,ourFashionStorewouldlooklikeafleamarket,
insteadofaclassicdepartmentstorewherethelatestcollectionsmeetyouatthe
entrance.
Toapproachthisproblem,weidentifystrategiccategoriesoffashionablecustomers
—customerswhoboughtoraddedtocartfashionbrandproducts—and
significantlyamplifytheirinfluencewhiledesigningthetrainingset.
AmazonSearch:TheJoyofRankingProducts,RecSys2016 11

12. Onlinemarketingandranking
eCommercesearchenginesincludebusinesslogicthatreflectsmarketingdecisions
Offlinemarketingandranking
eCommercebusinesseshavingbothonlineandphysicalpresencescreatesaunique
blendoforganizationalandinfrastructurechallenges.
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13. Regulatoryandbusinessrestrictions
Regulatoryandbusinessconstraintsgovernwhichproductscanbeshowntowhich
customers.
mosteCommercesiteshavebusinesslogicatthetimeofcheckout
todeterminewhetheraproductcanbepurchasedandshippedtoagivencustomer
e.g.
onlyadultscanvieworbuycertainproducts.
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14. Datalogistics
Dataplaysakeyroleinproductsearchandrecommendations.Serviceswherethe
eCommercewebsitehasmultiplevendorsbringindynamicswithregardstoqualityand
consistencyofthecontent,frauddetection,andpricing
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15. Thirdpartycontent.
SomeeCommercesitessuchasAmazon,Taobao,andeBayserveasaplaceforother
companiestosellproducts.
Thedataforthethirdparty
productsmayneedtobereformattedorsupplementedbeforeindexing.
e.g.
ifthebrandoftheproductisnotprovidedasstructureddatabythevendor,itmaybe
possibletoextractitfromtheproducttitle
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16. Volatileinventory
OneofthebiggestchallengesofeCommercesearchandrecommendationisthatthe
inventoryisconstantlychanging.
e.g.eBay
newitemneedtobeaddedquicklyintheindex.
offlinestoreinventoryandonlineinventorymustbesyncedinreal-time
Querysuggestionsarealsoaffectedbyvolatileinventoryastheymaysuggestqueries
thatnolongerreturnresults,creatingafrustratinguserexperience.
TheArchitectureofeBaySearch,SIGIReCom2017.
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17. Multi-modal
documents
IneCommercesearch,theindexed
documents,i.e.,
theproductscustomersarelookingfor,
arecombinationsofimages,
unstructuredtextsuchastitles,
descriptions,andreviews,and
structureddatasuchasprice,brand,
ratings,andsellerlocation
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18. eCommerceresearchareadeepdives
1.designofmatchingandrankingforeCommercesearch.
2.deepdiveintoconversationaleCommercethathaspromisetoenablethesmooth
shoppingexperienceprovidedbyexpertshopassistants
3.Wediscussissuesoffairness,confidentialityandtransparencywhichareattheheart
ofmaintainingcustomertrustwhileprovidingpersonalizedeCommerceexperiences.
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19. 1.Relevance:Matchingandranking
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20. Matching
Navigationalones(aserialnumber)
Needexactmatchestoproductserialnumbers,producttitlesorcategorynames
Longinformationalones(arebatteriesincludedwiththiswatch)
Needsemanticparsingandmoreelaborateindexingbeforetheycanbe
answered
Somequeriesmayrequireadifferentuserinterface;forexampleatabularlayoutis
betterforansweringcomparisonqueries
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21. Relevance
Originofdefinition:Relevancewasconsideredauniversal,dimensionlessquantity
Now:Nottobeuniversalbutinsteaduserdependent
eCommercerelevanceiscontext-dependentandithasfourdimensions
1.customer
2.time
3.query
4.contect(e.g.category)
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22. Matchingqueriesandproducts
eCommercesearchisasmuchaboutexplorationasitisaboutfindingthebestexact
match
mayneedcarefulcraftingofsynonymstomatchacustomer’svocabularytothatof
thebusiness
ATaxonomyofQueriesforE-commerceSearchWSDM2018bywalmart/
WhyDoPeopleBuySeeminglyIrrelevantItemsinVoiceProductSearch?,
WSDM2020byAmazon
alltypesofsearch,tokenization,includingwordbreaking,decompounding,and
punctuationhandling,lemmatizationorstemming,andstopwordidentificationare
importantforidentifyingrelevantproducts
Removingthevocaburarygapischallangingresearchtopic.
RemediesagainsttheVocabularyGapinInformationRetrieval 22

23. Queryunderstanding
Pseudo-relevancefeedback
TheImpactofQuerySuggestioninE-CommerceWebsites
Queryclickgraphs
ContextAwareQuerySuggestionbyMiningClick-throughandSessionData
Exploitingqueryreformulationsforwebsearchresultdiversification,WWW2010
MiningE-CommerceQueryRelationsusingCustomerInteractionNetworks,
WWW2018
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24. Queryunderstanding
Wordembeddings
QueryExpansionUsingWordEmbeddings,CIKM2016
Multi-modalmethodsthatcombinetextandvisualcues
ViTOR:LearningtoRankWebpagesBasedonVisualFeatures,WWW2019
ImprovingOutfitRecommendationwithCo-supervisionofFashionGeneration,
WWW2019
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25. Queryintentengines
parsethequerytoextractcatalogspecificattributes
LearningQueryIntentfromRegularizedClickGraphs,SIGIR2008
JointMap:JointQueryIntentUnderstandingForModelingIntentHierarchiesinE-
commerceSearch,WWW2019
e.g.
query"redsneakers"whichconvertedto...
{"color":"red", "shoe type":"running", "category":"shoes"}
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26. Queryintentengines
simplematchingofquerytermstoapredefinedsetofproductattributesto
moreelaboratesemanticmethods
QueryUnderstandingthroughKnowledge-BasedConceptualization
SemanticQueryUnderstanding
DeeperTextUnderstandingforIRwithContextualNeuralLanguageModeling
Ultimategoalofaqueryintentengineistoreturnstructured,personalizedqueriesforall
customerqueries.
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27. Ranking
Howtoranktheresultsshowntocustomersisoneofthemostcomplexissuesin
eCommerce.
Practitionershaveputeffortintoderivingasinglerankingfunctionthat
mixesbooleanortf.idf-basedrankingalgorithmswithothersignals,suchasrecency
orpopularity
e.g.
Query:"stripedt-shirts"
Mayrankhighlystripedproductsotherthant-shirts
Since striped 's IDF scoreishigherthan t-shirts
Numberofsignalsisincreasingtoimprovetherankingbut...
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28. Extendingtheproductrepresentation.
Documentshavemanyfeaturesbeyondhowcloselytheymatchthequeryterms
howmanytimestheyhave
beenpurchased
howmanytimestheyhavebeenclicked
theratioofclicksversuspurchases
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29. Rankingsignalsandoptimizationcriteria ️
eCommercesearchandrecommendationsystemsmustoptimizeformultiplecriteria
Multi-objectiverankingoptimizationforproductsearchusingstochasticlabel
aggregation,WWW2020byAmazon
oneencodingcustomerpreferencesandoneencodingbusinesspreferences.
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30. Rankingsignalsandoptimizationcriteria
Customersatisfactionismeasuredovermultiplesignals.
TutorialonOnlineUserEngagement:MetricsandOptimization,WWW2019
click-throughrate,hoveranddwelltime,satisfiedclicks,queryreformulations,
sessionlength,numberofqueriesbeforecheckout,add-to-baskets,purchases,
time-to-next-visit,productreturns,andcallstocustomerservice
BusinesssuccessismeasuredoverseveralKPIs
inventory-orientedmeasures,revenue-orientedmeasures,profit-oriented
measures,visitor-orientedmeasures,basket-orientedmeasures,
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31. Notallsignalsareequal
Objectivefunctionsovermultiplesignalscanbiastowardsmoreabundantsignals.
e.g.purchaseisamoreexplicitpreferenceindicatorthanaclickbutitismuchless
frequent.Apurchasethatwasnotreturnedisastrongersignalthanapurchasebutagainis
lessfrequent
Objectivefunctionsshouldtakeintoaccountthisdifferenceinsignalstrength
versussignalabundance
Tips:normalizationofsignal'svolumeisonesolution
NewsComments:Exploring,Modeling,andOnlinePrediction,ECIR2019
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32. Positive,negative,anddelayedfeedbackloops
Creatingfeedbackloopisreinforcementlearningparadigm
ReinforcementLearningtoRankinE-CommerceSearchEngine:Formalization,
Analysis,andApplication,KDD2018byAlibaba
longerfeedbackloopswherethefeedbackoccurswellafterthesystem
hasshownresultstotheuser
delayedfeedback,cold-startproblem.
LearningLatentVectorSpacesforProductSearch,CIKM2016
OnApplicationofLearningtoRankforE-CommerceSearch,SIGIR2017
AComparisonofCounterfactualandOnlineLearningtoRankfromUserInteractions.,
SIGIR2019
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33. PracticallimitationsofLearningtoRank
MosteCommercesearchenginesbasedonLtRworkintwosteps.
1.recall-orientedstep
2.precision-orientedstep
ThisimplementationofLtRhas
proventobeeffectiveintermsofIRandbusinessmetrics
PromotingRelevantResultsinTime-RankedMailSearch,WWW2017
LearningtoRankforFreshnessandRelevance,SIGIR2011
OnApplicationofLearningtoRankforE-CommerceSearch,SIGIR2017
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34. PracticallimitationsofLearningtoRank
challangeinLtR
1.broadexploratoryqueries
2.LtR’sissuewiththediscontinuityinusefulnessofSERP
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