Mining Shopping Patterns for Divergent Urban Regions by Incorporating Mobility Data

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by  Tianran Hu

Mining Shopping Patterns for Divergent Urban Regions by
Incorporating Mobility Data
∗
Tianran Hu∗1, Ruihua Song†2, Yingzi Wang†§3, Xing Xie†4, Jiebo Luo∗5
∗UniversityofRochester†MicrosoftResearch
§UniversityofScienceandTechnologyofChina
{1thu, 5jluo}@cs.rochester.edu,
{2song.ruihua, 4xing.xie}@microsoft.com, [email protected]
ABSTRACT shoppingpatternsindifferenturbanregions. Throughthepredic-
7 Whatpeoplebuyisanimportantaspectorviewoflifestyles.Study- tion,weareabletoreadthevariousdemandsoftheseregions,gain
1 ing people’s shopping patterns in different urban regions can not knowledgeoftheinfrastructuresofurbanareas,andmoreimpor-
0 onlyprovidevaluableinformationforvariouscommercialoppor- tantly,acquireabetterunderstandingofhumanlifestylegeograph-
2 tunities,butalsoenableabetterunderstandingabouturbaninfras- ically. Furthermore,thisstudycouldbeusedtosupportlocation-
based marketing, such as mobile advertising, outdoor exhibition
n tructureandurbanlifestyle. Inthispaper,weaimtopredictcity-
stands,andsoon.
a wideshoppingpatterns. Thisisachallengingtaskduetothespar-
MarketBasketAnalysisshowsthatconsumersusuallyhavede-
J sity of the available data – over 60% of the city regions are un-
3 knownfortheirshoppingrecords. Toaddressthisproblem,wein- mandsforagroupofproducts[4],andpeople’sdemandsarehighly
relatedtotheirlives[9]. Forexample,anITpractitionermayhave
2 corporateanotherimportantviewofhumanlifestyles,namelymo-
interestsindigitalproducts,computergames,aswellastechnical
bilitypatterns. Withinformationon“wherepeoplego”, weinfer
books. Motivatedbythisobservation, weconsidershoppingpat-
] “whatpeoplebuy”. Moreover,tomodeltherelationsbetweenre-
I gions, weexploitspatialinteractionsinourmethod. Tothatend, ternsascombinationsofinherentlyrelatedtypesofproducts. Our
S goalinthisstudyistopredictthelevelsofshoppingpatternsindif-
CollectiveMatrixFactorization(CMF)withaninteractionregular-
s. ization model is applied to fuse the data from multiple views or ferentcityregions. Comparedwithpredictingthelevelsofsingle
c typesof products, there aretwo mainbenefitsto doingso. First,
sources. Ourexperimentalresultshaveshownthatourmodelout-
[ sinceproductsareactuallycorrelated,thepredictionofagroupof
performsthebaselinemethodsontwostandardmetrics. Ourpre-
relatedproductsprovidesmorevaluableinformationforitemrec-
1 diction results on multiple shopping patterns reveal the divergent
ommendation and commercial planning. Second, since shopping
v demandsindifferenturbanregions,andthusreflectkeyfunctional
patternsaremorerelatedtothelifestylesofpeopleinaregionthan
9 characteristics of a city. Furthermore, we are able to extract the
singlecategoriesofproducts,predictingshoppingpatternscangive
3 connectionbetweenthetwoviewsoflifestyles,andachieveabet-
2 terornovelunderstandingofurbanlifestyles. usamorecomprehensiveunderstandingofurbanareas.
6 In this study, we collect geolocated logs of browsing products
0 onlinefromBeijing,andextractshoppingpatternsfortheregions
. Keywords inthecity.Althoughthevisitingvolumestoproductwebpagesare
1
nottheexactproducttradingvolumes, theyindicatethedemands
0 Shopping Patterns, Mobility Patterns, Urban Computing, Multi-
fordifferentproducts. Therefore,thebrowsingrecordscanbere-
7 viewLifestyles
gardedasreasonableproxyofconsumershoppingbehaviors. Ac-
1
cordingtothestudyonhumanmobility[28], theprobabilityofa
: 1. INTRODUCTION
v personmovingacertaindistancedropsdramaticallyasthedistance
i Studies on land use suggest a variety of types of city regions, exceeds1kminurbanarea.Therefore,wedefinearegionasa1km
X
suchasresidential,industrialandothers[27,3]. Suchdivergence by1kmgridalongthelatitudeandlongitudelinesinacity.Wefor-
r ofcityregionfunctionality [29]naturallyleadstothevariousshop- malizeregion-basedshoppingpatternsasamatrix,andanelement
a
pingpatternsindifferentregions. Dailysupplies,forexample,are in the matrix denotes the level of a shopping pattern in a region.
usuallyneededinresidentialquarters, whileofficeappliancesare Ourtaskistocompletethemissingvaluesinthematrix. Thema-
more popular in business areas. In this paper, we aim to predict trixsuffersfromaseveresparsityproblem–over60%ofrowsare
empty. Inotherwords,wedonothaveanyshoppinginformation
∗Thisworkwasconductedwhilethefirstauthorwasdoingintern- ofmorethan60%regionsinthecity. Toaddressthisproblem,we
shipatMicrosoftResearchAsia seekhelpfromothertypesoflifestyleinformation.
Lifestyle is defined as “the way in which a person or a group
ofpeoplelive”inMerriam-WebsterDictionary1. Therearemany
Permissiontomakedigitalorhardcopiesofallorpartofthisworkforpersonalor
classroomuseisgrantedwithoutfeeprovidedthatcopiesarenotmadeordistributed aspects or views to describe how people live [24, 30], including
forprofitorcommercialadvantageandthatcopiesbearthisnoticeandthefullcita- shopping patterns and mobility patterns. For example, a teenage
tiononthefirstpage. Copyrightsforcomponentsofthisworkownedbyothersthan
studentmayhaveacertainshoppingpattern,suchasbooks,digi-
ACMmustbehonored.Abstractingwithcreditispermitted.Tocopyotherwise,orre-
publish,topostonserversortoredistributetolists,requirespriorspecificpermission talproducts,andsoon. Similartoshoppingpatterns,weconsider
and/[email protected]. amobilitypatternasagroupofinherentlyrelatedPOIcategories.
CIKM’16,October24-28,2016,Indianapolis,IN,USA Wecanalsoseeacertainmobilitypatternofthestudent, suchas
(cid:13)c 2016ACM.ISBN978-1-4503-4073-1/16/10...$15.00
DOI:http://dx.doi.org/10.1145/2983323.2983803 1http://www.merriam-webster.com/dictionary/lifestyle/
• Multi-viewLifestyles: Westudytheconnectionbetweendif-
ferentviewsoflifestyleparticularlyintermsofshoppingand
mobility patterns. The reasonable mappings between these
viewsguaranteetheeffectivenessofincorporatingmobility
patternstopredicttheshoppingpatterns.Moreover,interest-
ingmappingsbetweentwoviewsprovideabetterandnovel
understandingofhumanlifestylesinthecity.
• City-wideshoppingpatternsmodeling:Weextractrepresen-
tativeshoppingpatternsofregionsinurbanareas,andpredict
Figure 1: The illustration of the geographical distributions of a
thelevelofeachshoppingpattern.Ourresultsshowthedra-
shopping pattern (left) and a mobility pattern (right) of the same
matic divergence in shopping demands over the city. This
regiononalocalmapinBeijing.
workshedslightonabetterunderstandingoftheurbanin-
dormitory,library,andsoon.Multipleviewsofalifestyleareactu- frastructure, and provides valuable information for various
allyconnected,andthisconnectionisreflectedontheirgeograph- businessopportunities.
icaldistributions. Figure1showsthegeographicaldistributionsof
ashoppingpatternextractedfromthebrowsinglogsandamobil- 2. RELATEDWORK
itypatternextractedfromcheck-indatainaregion. Thetopthree
most browsed product categories of the illustrated shopping pat- 2.1 UrbanRegionDivergences
tern(left)areLaptops,OfficeAppliances&Stationery,andTablets.
Previousworksonurbanlandusesuggestvarioustypesofcity
ThetopthreemostcheckedinPOIcategoriesofthemobilitypat-
regions [27, 3]. Singh et. al report their findings on noise lev-
tern (right) are Offices, Convenience Stores, and Commute. We
elsofthreetypesofcityregions: residential,industrial,andcom-
observe that the geographical distributions of these two patterns
mercial [22]. As social media has been being widely adopted in
arequitesimilar,andtheybothreachthehighestlevelsinthesame
people’sdailylives,socialmediadatabecomesanimportantdata
block. Inotherwords,manypeoplecheckinatoffices(mobility)
source for related research. For example, geolocated tweets are
inthisblock,meanwhile,manypeopleshowhighdemandsforof-
usedin[10]tocharacterizeurbanlandscapes.Inthiswork,theau-
fice appliances (shopping) in the same block. The geographical
thorsusetweetcontentstoidentifybusiness,leisure,nightlife,and
connection of these two views of the lifestyle inspires this work:
residentialregionsinNewYorkCity.
connecting shopping and mobility patterns, and utilizing the mo-
Sinceurbandataisusuallynoisyandsparse,researchershaveto
bilitypatternsinaregiontopredicttheshoppingpatternsthere.To
combineseveraldatasourcestodigmeaningfulknowledge. Yuan
achievethis,welearnregionmobilitypatternsfromcheck-indata
et. al[29]discoverfunctionalzonesinBeijing. Inthiswork,the
ofapopularLocationBasedSocialNetwork(LNSB)inChina,and
authorsclusterregionsintoseveralfunctionalgroupsbasedontheir
employaCollectiveMatrixFactorization(CMF)[21]modeltofuse
transitionpatterns.POIinformationinaregionisusedasmetadata
thesetwoviews.
fortheregioninthiswork. In[32],Yuet. alreporttheirworkon
However, considering the lifestyles of regions in a city sepa-
predictingthenoiselevelsofNewYorkCity. Cityroadstructures,
rately is inappropriate. Regions in a city are never isolated, as
POIdistribution,andcheck-indataareusedinthiswork. Interest-
they are connected by people transitions instead [6, 33]. Intu-
ingcorrelationsarefoundbetweenmultipledatasourcesandnoise
itively,thecommutingflowsbetweenregionsalsoimplytheflows
level. For example, universities in a region are quite responsible
of lifestyles. For example, it is reasonable to assume that con-
for“party&loudspeak”noise. Similarly,theauthorsof[20]in-
sumers’demandsaffecttheshoppingpatternsinboththeregions
troduceroadnetworkandPOIlocationstoinferthetrafficvolume
wheretheyliveandwork. Weassumethatthestrongertheinter-
inaregion.[31]givesanicesurveyoncross-domaindatafusionin
actions between two regions are, the more alike the lifestyles in
urbancomputing.
both regions are. We define the interaction from a region to an-
otherregionastheprobabilityofapersonmovingfromtheformer 2.2 HumanLifestyles
to the latter. We first collect two large human transition datasets
Human lifestyles are well studied in sociology [2, 11]. In re-
inBeijing,andthenapplyagravitymodel[26]toquantifythein-
cent years, researchers have successfully utilized social media in
teractions. Weincorporatetheinteractionsbetweenregionsasan
researchventuresrelatedtolifestyleanalysis[18,1].Forexample,
regularizationterminouroverallmodel. Incomparisonwithsev-
Noulasetal.of[15]useFoursquaredatatodiscoverthebehavioral
eralbaselinemethods,ourmodelimprovestheaccuracyby10.8%
habitsofresidentsinLondon. Basedontweetcontents,Sadieket
and12.6%intermsoftheMeanAbsoluteError(MAE)with80%
al.buildalanguagemodeltodetectthehealthofindividuals[17].
and90%trainingdata,respectively.
Muchefforthasbeenmadetolinkingmultipletypesoflifestyles.
Ourmaincontributionsaresummarizedasfollows:
Cranshawet.al[7]reportthathumanmobilitypatternsarestrongly
• Dealing with data sparsity: We incorporate mobility pat- connectedtotheirsocialnetworks. In[34],theauthorsusecheck-
terns of regions as complementary information to address in data to infer users’ demographic features. Furthermore, Yuan
thesparsityissueofourshoppingdata. Weleveragetheun- et. al linked several aspects of human lives, such as occupation,
derlyingconnectionbetweenmobilityandshoppingpatterns, education level, and mobility patterns by studying the data from
anduse“wherepeoplego”tohelpinfer“whatpeoplebuy”. severalsocialmediaplatforms[30]. Inspiredbytheseworks,our
Moreover, to model the relations between regions and fur- keyideainthisworkisusinginformationofoneview(mobility)of
thermakeupforthesparedata,weconsidertheinteractions lifestylesofaregiontoinfertheotherone(shopping).
between regions in our overall model. A hybrid model is
2.3 MatrixFactorizationTechniques
proposed to utilize several data sources or views. The ex-
perimental results validate that our model outperforms the MatrixFactorization(MF)techniques,alsoknownasMatrixDe-
baselinemethodsaccordingtotwostandardmetrics. composition,arewidelyappliedinrecommendationsystemstopre-
dictmissingvaluesinmatrices[19]. AbasicMFmodelfactorizes R correspondingtollatentlifestyles. TheithcolumnofR,for
l l
thetargetmatrixintoalatentpatternmatrixandacoefficientma- example,denotestheweightsofregionsontheithrowofV(cid:62)when
1
trix,andusestheproductofthemastheestimationtomissingval- usingthesetwomatricestorecoverR .Meanwhile,ithcolumnof
s
ues. R alsodenotestheweightsoftheregionsontheithrowofV(cid:62)
l 2
However,thisapproachusuallysuffersfromdatasparsityprob- whenusingthesetwomatricestorecoverR .Thisintuitivelyim-
m
lems[12]–theinformationcontainedintheoriginalmatrixisnot pliesthemappingbetweenithrowinV(cid:62) andV(cid:62) –theyarethe
1 2
enough for a precise factorization. Our dataset has such a prob- twoviewsofthesamelatentlifestyle.
lem – a majority of rows in our target matrix are empty. Many
techniqueshavebeendevelopedtodealwiththisproblem[16,35],
includingCollectiveMatrixFactorization(CMF)[21]. InaCMF
model, supplementary information is provided in terms of matri-
ces. Insteadofdecomposingonlythetargetmatrix,severalmatri-
cesaredecomposedtogether.Bydoingso,supplementaryinforma-
tionhelpstomodifythefactorizationresults,andproduceabetter
predictionresult.Inthiswork,weapplyaCMFmodeltocombine
shopping and mobility patterns. Social regularization is reported
in [14]. In this work, Ma et. al consider the social connections
betweenusersinarecommendationsystem,andintroducethecon-
nectionsintoatransitionalMFmodelusingasocialregularization
term. Weintroduceregioninteractionsintoourmodelusinganin-
teractionregularizationterm.Theinteractionsarecalculatedusing
agravitymodel[26].Thismodelassumesthehumanmobilityina
cityfollowsNewton’slawofgravity,andiswidelyappliedinur-
bangeography.Wanget.al[25]employthismodeltocalculatethe
influencebetweenregionsintheirPOIrecommendationsystem.
3. OVERVIEW
Beforeweintroducingourmodel,wefirstgothroughthework-
flow of this study in Figure 2. There are two main parts to this
work:1)shoppingandmobilitypatternsextraction,and2)predict-
ingshoppingpatternsusingacollectivematrixfactorizationmodel
withinteractionregularization.
Thefirstpartstartsfromtherawbrowsingdataandrawcheck-in
data. We extract n shopping patterns (matrix P ) and m mobil-
s
ity patterns (matrix P ) from these two datasets. n and m are
m
input parameters. From this process, we also obtain the weights
onshoppingpatternsofdifferentlocations,aswellastheweights
Figure2:Ourframework
onmobilitypatternsofLBSNusersintheformoftwocoefficient
matrices.
Thesecondpartstartsfromaggregatingthelocationsandusers 4. SHOPPINGANDMOBILITYPATTERNS
into regions. After the aggregation, we obtain a region shopping
OFREGIONS
patternmatrixR ,ar bynmatrix,andamobilitypatternmatrix
s
R ,arbymmatrix.Here,rdenotesthenumberofregionsinthe Wedefineashoppingpatternasagroupofrelatedproductcat-
m
city. Also, wepreparetheinteractionmatrixQ, ar byr matrix, egories. Forexample,Furniture,HomeTextiles,LargeHomeAp-
which contains the interaction information between regions. We pliances,andsoonareextractedasashoppingpatterninthiswork.
thenapplycollectivematrixfactorizationwithinteractionregular- AmobilitypatternisagroupofrelatedPOIcategories,suchasa
izationonR ,R ,andQ.ThisisbasedontheassumptionthatP combination of School Dormitories, School Libraries, and so on.
s m s
canbeapproximatelyfactorizedtoR ×V(cid:62), meanwhile, P to Shopping and mobility patterns are basic bricks of two views of
l 1 m
R ×V(cid:62). Threematricesaregeneratedfromthisprocess: 1)R, lifestyles.Inthissectionweexplainhowweextractthesepatterns.
l 2 l
arbylregionlatentlifestylematrix,whichindicatestheweights
4.1 ShoppingPatternsExtraction
onlatentlifestylesofregions. lisaninputparameterdenotingthe
numberoflatentlifestyles. 2)V(cid:62),albynmatrix. Thismatrixis Wecollectthebrowsinglogofshoppingwebsitesfromthecity
1
theshoppingviewoflatentlifestyles.3)V(cid:62),albymmatrix,con- of Beijing. Each log record is associated with a product cate-
2
tainsthemobilityviewoflatentlifestyles. Wepredictthemissing goryandalocationidentifier. Therearenouseridentifiersinthe
valuesinR withthecorrespondingelementsoftheproductofR data.Moreover,thelocationidentifiersarenotthepreciselocations
s l
andV(cid:62). whereconsumersvisitproductwebpages,butthelocationsofthe
1
Thebasicideabehindcollectivefactorizationhereisassuming networkcelltowerstheirdevicesconnectto.
thattheweightsonshoppingpatternsR andmobilitypatternsR Sincewecannotidentifyusersinourdata,asacompromise,we
s m
ofregionsarebothgeneratedbyllatentlifestyles.EachrowinV(cid:62) usethedatafromanetworkcelltowerasthebasicunitforshop-
1
explainsalatentlifestylewithweightsonshoppingpatterns,while pingpatternmining. Aacelltowercoversarelativelysmallarea,
a row in V(cid:62) explains a latent lifestyle with weights on mobility hence,weassumethatthepeopleconnectedtoatowerhavesimi-
2
patterns.Thisprocessalsorevealsexquisitemappingsbetweenhu- larshoppingpatterns. Notethat,acityregioncouldcontainmul-
manshoppingbehaviorsandmovements. Therearel columnsin tiple or no network cell towers. We first aggregate the browsing
recordsfromthesamelocationsintoavector,wenamethisvector form Jiepang2, a popular LBNS in China. Each check-in is as-
thebrowsingvectorofalocation. Thelengthofabrowsingvec- sociated with a user ID, a POI category, and a latitude and lon-
toristhenumberofproductcategories,andthevalueofacertain gitude pair. We take a user as the basic unit for mobility pattern
elementisthebrowsingamounttothatproductcategoryfromalo- mining. Theprocessissimilartoshoppingpatternextraction. We
cation. Thebrowsingvectorsareaccumulatedtoformabrowsing firstaggregateusers’check-insintovectors, namedmobilityvec-
matrix. A row in this matrix indicates a location (a network cell tors. Mobility vectors record users’ amounts of check-ins of all
tower),andacolumnindicatesaproductcategory.Wethenapplya POI categories. We then accumulate mobility vectors into a mo-
NonnegativeMatrixFactorization(NMF)modelonthisbrowsing bility matrix, and apply NMF on it to extract a mobility pattern
matrix, since NMF leads to interpretable results [13]. We obtain matrixP ∈ Rm×cm,andacoefficientmatrix. Here,mdenotes
m
tworesultmatricesthroughtheprocesses: ashoppingpatternma- theamountofmobilitypatterns,andc isthenumberofPOIcate-
m
trixP ∈ Rn×cs andacoefficientmatrix,wherenisthenumber gories.P describesthewaysinwhichpeoplelivefromaviewof
s m
ofshoppingpatterns, andc isthenumberofproductcategories. spatialactivities,andthecoefficientmatrixrecordstheweightsof
s
ArowofP indicatestheweightsonproductcategoriesofashop- usersonthesemobilitypatterns.
s
pingpattern. Thecoefficientmatrixindicatestheweightsoneach Figure 3 (lower part) shows four mobility patterns with their
shoppingpatternoflocations. mostweightedPOIcategories.Similartoshoppingpatterns,amo-
bility pattern also focuses on a specific group of categories. The
first pattern indicates a mobility pattern centered at Offices, fol-
lowedbyConvenientStoresandCommute. Thesecondmobility
styleisdominatedbyCinemas. ThenexttwocategoriesareVideo
Game Halls and Board Games. This is clearly an entertainment
relatedmobilitypattern.Thethirdoneismoreaboutoutdooractiv-
ities,whichassignshigherweightstoMuseum&Exhibition. The
lastmobilitypatternmainlyfocusesonPOIsrelatedtoschoolsor
universities. Dormitories, Laboratories, and Libraries have high
weightsinthispattern.
Since LBNS users may leave footprints in several regions, it
makes more sense to allocate the sameproportion of user mobil-
itypatternstotheregionstheycheckedinwiththepercentagesof
activitiestheyspentintheseregions.Forexample,ifauserspends
40% check-ins in a region, then this user contributes 40% of her
mobilitypatternstothisregion. Therefore,wedefinethemobility
Figure 3: The top weighed categories of four shopping patterns patternsofaregionastheweightedsumofalltheusers’mobility
(upper), andfourmobilitypatterns(lower). Horizontalaxisesin- patterns,whocheckedinthisregionsbefore. MatrixRm ∈Rr×m
dicateproductcategoriesandPOIcategories,respectively.“Accs”, isthenformed,wheretheelementonithrowandjthcolumnde-
“Conv”, “Dorm”, “APLS”,“Intel”, “Prods”, and“Dec”, areshort notesithregion’sweightonjthmobilitypattern.Formally,
for “Accessories”, “Covenient”, “Dormiatroies”, “Applicances”,
“Intellegent”,“Products”,and“Decoration”,respectively. (cid:88)
R = w ×u , (1)
mi,j k,i k,j
InFigure3weplotthetopweightedproductcategoriesofsev- k
eralshoppingpatterns(upperpart). Pleasenotethattheamountof where wk,i is the percentage of activity which user k spends in
categories of products and POIs is more than what we display in regioni,anduk,j isthisuser’sweightonjthmobilitypattern.
thefigure.Wecanseethateachshoppingpatternhasaspecificem-
phasisonagroupofrelatedproducts,whichnaturallyrepresentsa 5. HYBRID MODEL FOR SHOPPING PAT-
classofdemandsofpeople. Thefirstshoppingpatternmainlyfo- TERNPREDICTION
cusesondigitalproducts.Tablets,PhotographyProducts,andDig-
R containstheshoppingpatterninformationofregions,andis
ital Accessories are the top three most weighted categories. The s
asparsematrix. Ourobjectiveistofilltheemptiessoastopredict
secondpatternisdominatedbyWomen’sClothing,andothercate-
shoppingpatternsofmissingregions. Inthissection, weapplya
goriessuchasUnderwearandFemaleShoeshavecertainsharesin
CollectiveMatrixFactorizationmodelwithaninteractionregular-
thepattern. Thethirdpatternshowninthefigureismainlyabout
izationtermtoachievethegoal.
homearrangementsanddecorations,Furniture,HomeTextiles,and
LargeHomeAppliancesarethemostpopularproductcategoriesin 5.1 CollectiveMatrixFactorization
thispattern.ThelastshoppingpatternhashighestweightsonBaby
Wetreatshoppingandmobilitypatternsinaregionastwodiffer-
Products,Toys,andChildren’sBooks.
entviewsgeneratedbysamelifestyles.Inotherwords,weassume
Wethensumuptheweightsonshoppingpatternsofalltheloca-
R andR arebothgeneratedfromamatrixcontainingthelatent
tionsinaregiontoobtaintheweightsontheshoppingpatternsof s m
aregion. TheresultisrepresentedbyamatrixR ∈Rr×n,where lifestyleinformationofeachregion.Formally,
s
risthenumberofregionsinthecity.Therowsofthismatrixindi-
cateregions,andcolumnsrepresentshoppingpatterns.Sincemany R ≈RV(cid:62) (2)
s l 1
regions do not contain records at all, R is a sparse matrix. Our
s R ≈RV(cid:62) (3)
goalinthisworkistofillintheemptyelementsofR . m l 2
s
whereR ∈Rr×ldenotestheweightsonllatentlifestylesofeach
l
4.2 MobilityPatternsExtraction region. V(cid:62) ∈ Rl×n explains l latent lifestyles with n shopping
1
To extract human mobility patterns, we collect check-in data 2http://www.jiepang.com/
patterns. Similarly, V(cid:62) ∈ Rl×m explainsl latentlifestyleswith
2
mshoppingpatterns. Guidedbytheextramobilityinformationof
R ,weobtainmoreaccurateR andV .Thepredictionofmissing
m l 1
valuesinR isachievedbymultiplyingR andV .Followingthis
s l 1
idea, we apply a collective matrix factorization model to decom-
pose R and R together. The objective function is formulated
s m
as:
1 λ
L(R,V ,V )= (cid:107)I◦(R −RV(cid:62))(cid:107)2 + 1(cid:107)R −RV(cid:62)(cid:107)2
l 1 2 2 s l 1 F 2 m l 2 F Figure4: TheheatmapoftheinteractionsbetweenregionXwith
λ otherregionsinthecity.Adarkercoloredregionindicatesahigher
+ 2((cid:107)R(cid:107)2 +(cid:107)V (cid:107)2 +(cid:107)V (cid:107)2),
2 l F 1 F 2 F probabilityofpeopleinthisregionmovingtoregionX.
(4)
objectivefunctions. Asmentionedintheintroduction,interactions
whereoperator“◦”meanstheentry-wiseproduct,(cid:107).(cid:107)2F denotesthe between regions transit lifestyles too. The more interactions be-
Frobeniusnorm,andIisanindicatormatrixwithitsentryIij =0 tweentworegions,themorealiketheirlifestylesare.Basedonthis
ifRsij ismissing. Thelastformisforpreventingoverfitting. λ1 intuition,weproposetheinteractionregularizationtermas:
andλ areparmeterscontrollingthecontributionofeachpart.
2
An interesting knowledge we can learn from this approach is
tbhoethmaaspspoicniagtebdetwwiethenthtewiothviceowlus.mTnhineiRth,rwowhicohfiVm1(cid:62)pliaensdthVa2t(cid:62)thaerye α2 (cid:88)r (cid:107)Rli − 12 (cid:88) (cid:80)rj(cid:80)=1rQ∗(Qj,∗i()j,×i)Rlj(cid:107)2F, (7)
l i=1 ∗∈A,B j=1
representthesamelatentlifestyle. However,onlyassociatingtwo
views of lifestyles ignores the interactions between regions. We where Q∗(j,i) denotes the interaction from region j to i by bus
quantifytheinteractionsusingagravitymodel,andintroducethis ortaxi,andRli indicatesthelifestylesofithregion. Wetaketwo
factorintheobjectivefunctionasaregularizationterm. traffic types equally in the regularization, and average two types
ofinteractionsasthefinalinteraction. αisaparameterthatindi-
5.2 City-wideInteractionRegularization
catestheimportanceofthisregularizationterm. Thistermlimits
thedifferencebetweenregions’lifestylesaccordingtotheirinter-
5.2.1 GravityModel
actions. Inotherwords,alargeQ(j,i)leadstoastronginfluence
Wedefinetheinteractionfromregionitoregionjastheproba- fromregionj toregioni. Asaresultofintroducingthisterm,the
bilityofapersonmovingfromitoj. Tocomputethisprobability, cross-regioninfluenceisincorporatetoourmodel.
weemployandadaptagravitymodel[26],whichiswidelyadopted
5.3 HybridModel
inmobilityanalyticsforalargepopulation.
Thegravitymodelstatesthatthecommutingflowsfromregion Wecombinethecollectivematrixfactorizationmodelandinter-
i to j, denoted as qi,j, are determined by 1) Oi, the number of actionregularizationtermtoobtainourfinalobjectivefunction:
individualsleavingregioni,2)D ,thenumberofindividualsar-
j
rivingatregionj,and3)thedistancebetweentworegions,through
L(R,V ,V )
agravity-likelaw: l 1 2
1 λ
= (cid:107)I◦(R −RV(cid:62))(cid:107)2 + 1(cid:107)R −RV(cid:62)(cid:107)2
(O )a(D )b 2 s l 1 F 2 m l 2 F
qi,j =cexp(ig·disji,j), (5) + α2 (cid:88)r (cid:107)Rli − 12 (cid:88) (cid:80)rj(cid:80)=1rQ(jQ,i()j,×i)Rlj(cid:107)2F
where c is a constant, a and b are coefficients of Oi and Dj, re- i=1 ∗∈A,B j=1
spectively.gisaparameterthattunesthedecaybydistance.When λ
i=j,thismodelcomputestheinteractionswithinaregion. + 22((cid:107)Rl(cid:107)2F +(cid:107)V1(cid:107)2F +(cid:107)V2(cid:107)2F), (8)
Wefitthismodelusingobservedtransitiondatatoestimatethe
coefficientsa,b,andg. Applyingalogarithmictransformationto Therearegenerallynoclosed-formsolutionsforminimizingthe
bothsideof(5),weobtainthefollowingexpression: above objective function. Therefore, we search for a local mini-
mum by applying gradient descent in the lifestyle vector of each
regionR andmatrixV ,V asfollows
lnq =alnO +blnD −g·dis +lnc (6) li 1 2
i,j i j i,j
Amultivariateregressionmethod[5]isthenemployedon(6)to r
estimatethecoefficientsbasedontheobserveddata. Weconsider ∂L =(cid:88)I (R V(cid:62)−R )V(cid:62)+λ R
∂R i,j li 1j si,j 1j 2 li
twotypesofcommutinginacity:bytaxiandbybus.Basedthem, li j=1
iwnegcthoemipnuteteraqciBt,ijoninbdyicatatixnig. Fthigeuirnete4raschtoiownsbtyhebiunst,earancdtiqoiAn,jbientdwiceaetn- +α(cid:88)r (Rli − 12 (cid:88) (cid:80)rj(cid:80)=1rQ∗(Qj,∗i()j,×i)Rlj)
aregion(pointXonthemap)andotherregionsinthecity.Wethen j=1 ∗∈A,B j=1
fillthequantifiedinteractionsintomatricesQBandQA,wherethe ∂L
elementintheithrowandjthcolumnisthespatialinteractionfrom ∂V =[I◦(RlV1(cid:62)−Rs)](cid:62)Rl+λ2V1
1
regionitoregionjbybusandtaxi,respectively.
∂L
=λ (RV(cid:62)−R )(cid:62)R +λ V . (9)
5.2.2 City-wideInteractionRegularization ∂V 1 l 2 m l 2 2
2
Inspiredbythesocialregularizationreportedin[14],weformu- Byoptimizingtheobjectivefunction,weobtainR,V ,andV .
l 1 2
latethetransitionsbetweenregionsasaregularizationterminour AccordingtoEqu.2,wepredictthemissingvaluesinR withthe
s
productofR andV(cid:62). Also,thecorrespondingrowsofV(cid:62) and directly.FollowingtheuserIDs,wedownloadtheprofilesof
s 1 1
V(cid:62) explain same lifestyles from two views. We summarize our theseusers.Aprofilecontainsbasicinformationaboutauser
1
algorithminAlgorithm1. includingtheirhomecity. Wethencollecttheusers,whose
home city is Beijing, and aggregate their check-ins. After
Algorithm1:OptimizationoftheHybridModel doing so, we obtain 1.5 million check-ins generated by 43
thousandusers.
Input:regionshoppingpatternmatrixR ,regionmobility
s
patternmatrixRm,regioninteractionmatrixQAand • busdataset:Thisdatasetcontains3millionbus-triprecords.
QB,andparameters:λ1,λ2,andα ThedatawascollectedinBeijingfromAugust2012toMay
Output:regionlifestylepatternmatrixRl,lifestyle-shopping 2013, and each record is a 5-tuple consisting of a user ID,
matrixV1,andlifestyle-mobilitymatrixV2 boardingandalightinglocations,andalightingtime.
1 initializeRl,V1,andV2;
• taxi dataset: This dataset consists of 1.9 million taxi trips
2 //Tismaximumiterations,(cid:15)isastoppingthreshold
inBeijingfromMarch2011toAugust2011. Eachtripin-
3 whilet<T andLt−Lt+1 >(cid:15)do
cludesthelocationoftheoriginanddestination, aswellas
4 calculatetheobjectivefunctionLtbasedonEqu.8;
theboardingandalightingtime.
5 calculate ∂L , ∂L,and ∂L basedonEqu.9;
6 γ=1; ∂Rli ∂V1 ∂V2 Thecheck-indataandthetransitiondataarenotasup-to-dateas
7 //searchforthemaximallearningrate; ourbrowsingdatadataset.SincetheurbanstructureofBeijinghas
8 while notchangedmuchinrecentyears,thisallowsustoapplythesedata
L(R −γ ∂L ,V −γ ∂L,V −γ ∂L)≥L(R,V ,V ) ascomplementaryinformationsourcesinourprediction.
li ∂Rli 1 ∂V1 2 ∂V2 l 1 2 6.2 Metrics
do
9 γ=γ/2; Weapplytwopopularmetricstoevaluateourmodel,theMean
10 updateRli,V1,andV2basedonEqu.9; AbsoluteError(MAE)andtheRootMeanSquareError(RMSE).
11 t=t+1; RMSEandMEAaredefinedas:
12 returnRl,V1,andV2 (cid:115)
RMSE = 1 (cid:88)(R −Rˆ )2, (10)
T si,j si,j
i,j
6. EVALUATION
6.1 Data MEA= 1 (cid:88)|R −Rˆ |, (11)
T si,j si,j
Wepartthecityinto1kmby1kmgridsalignedwiththelatitude i,j
andlongitudelines.WetaketheurbanareawithinBeijing5thRing whereRsi,j denotesthetruevalueofjthshoppingpatterninith
Road,andthereare29×30=870gridsintotal,andwedonothave region,andRˆ istheestimationofit. T isthenumberoftested
si,j
anybrowsingrecordsfor548(62.9%)ofthem. Eachgridistaken values. Clearly, a lower RMSE or MEA value indicates a better
asaregion. UseronlinebrowsingdataisusedforextractingRs, performance.
theshoppingpatternsofeachregion.Check-indatafromapopular
6.3 Baselines
LBSNisusedtoextractR ,themobilitypatternsofregions. To
m
learntheinteractionsbetweenregions,weutilizetwolargedatasets Wecompareourmodelwiththreebaselinesdescribedasfollows.
oftaxiandbustransitionsinthecity.Wedescribethesedatasetsas
• MatrixFactorization(MF):thismethoddecomposesR into
follows: s
R andV directly,andusestheproductoftworesultsma-
l 1
• onlinebrowsingdataset:Wecollecttheonlineshoppingbrows- tricesastheprediction.
ing logs of Internet Explorer (IE) users, who have granted
• CollectiveMatrixFactorization(CMF):inthisbaseline,we
permissionforrecordingtheirlogdata. Ourdatasetiscol-
factorizeR andR togethertoobtainR,V ,andV ,with-
lectedfromNovtoDec2015,andcontainsallthelogrecords s m l 1 2
outusinginteractioninformation.
generatedinBeijing. Eachrecordisassociatedwithaprod-
uct category and a location identifier. There are more than • CMF with neighboring information: since a region usually
250productcategoriesintotal. Manyconstraintsaresetup hasthemostinteractionswiththeregionssurroundedit, in
inthecollectionprocesstoprotectuserprivacy,whichmake this method, we assume that a region is only affected by
thisstudymorechallenging. First,therearenouseridenti- neighboringregions.Accordingly,theinteractiontermisre-
tiesinthedata. Asaresult,wearenotabletotraceanyper- formedas:
sporencailsbercoowosridnignahtiesst,obryu.tAthlesol,otchaetilooncsaotifonnestowforrekcocredllstaorweenrost. α(cid:88)r (cid:107)R − (cid:80)j∈neighborsRlj(cid:107)2, (12)
2 li (cid:107)neighbors(cid:107) F
Thisconstraintpreventsusfromlocatingwherethebrowsing
i=1
behaviorsaretakingplaceexactly.
where neighbors is the set of indexes of the regions that
• check-indataset:Wefirstcollectmorethan2millioncheck- surroundregioni,and(cid:107)neighbors(cid:107)denotestheamountof
in data in Bejing from Aug 2011 to Jan 2013. A check-in neighbors.Wetaketheeightsurroundingregionsastheneigh-
containsatime-stamp,alocation,andauserID.Eachloca- borsofaregion.Thecomparisonbetweenourmodelandthis
tionisassociatedwithaPOIcategory,andtherearearound baselineshowstheadvantageofintroducingtheinteractions
200cateogiresintotal.Sincetouristsgeneratealotofcheck- between all regions over simply considering local interac-
insinalargecitylikeBeijing,wecannotusethesecheck-ins tionswithneighbors.
TrainingSize 80% 90% Lifestyle1describesthetypicallifeofurbanwhite-collars. The
Metrics RME MEA RME MEA topweightedmobilitypatternsmainlyfocusonwork(Office,Con-
MF 0.235 0.443 0.213 0.420 venient Stores, and Commute), home (Apartment & Communi-
CMF 0.230 0.408 0.203 0.400 ties,Commute),andshoppingplaces(ShoppingMalls,Cosmetics
improve 2.12% 7.90% 4.69% 4.76% Stores,andSportingStores). Interestingly,insteadofofficeappli-
CMF+N 0.226 0.398 0.200 0.374 ances,themostpopularshoppingpatternisaboutwomen’scloth-
improve 1.73% 2.5% 1.47% 6.5% ing, underwear, and women shoes. This may indicate the online
CMF+I 0.223 0.394 0.197 0.367 shopping behaviors of office clerks while working. The second
improve 1.30% 1.00% 1.50% 1.87% shopping pattern consists of Laptops, Office Appliances & Sta-
Total 5.1% 10.81% 7.5% 12.61% tionery,andTablets,whichareusuallyneededforwork.Themap-
pingbetweenthesetwoviewsofthislifestyleexplainstheoverlap-
Table1:RMEandMEAvaluesofeachmodel.“CMF+N”denotes pingofthetwogeographicaldistributionsshowninFigure1. The
CMFmodelplusneighboringinformation,and“CMF+I”denotes demandsforofficeappliancesandthecheck-insatofficescanbe
CMF model plus interaction information. “improve” denotes the explainedasgeneratedbythislatentlifestyle. Therefore,thehigh
improvementofaccuracyofeachmodelcomparedwiththemodel levelofthislifestyleinaregionnaturallyresultsinthehighlevels
beforeit. “Total”indicatestheimprovementofaccuracyusingour ofbothpatterns.
modelcomparedwithMFmodel. Lifestyle2showsastrongemphasisondigitalandelectricprod-
uctsfromitsshoppingview.Thetopshoppingpatternofthislifestyle
6.4 Results contains Tablets, Photography, and Digital Accessories, and the
thirdshoppingpatterncontainsPeripheralandNetworkProducts.
Inmanypreviousworksonmatrixfactorization[14],testingdata
We can see from its view of mobility that, a group of POI cate-
are formed by randomly selected non-zero elements. These se-
goriesleadbyDigitalApplianceStoresisoneofthetopmobility
lectednon-zeroelementsarecomparedwiththepredictionresults
patterns. Also, this lifestyle has a high demand for books – the
toevaluatemodels.Inourproblem,R containsmanyemptyrows.
s secondshoppingpatternisdominatedbyBooks.Correspondingly,
To test if our model is able to predict the regions that we do not
weobservethatthemostpopularmobilitypatterninthislifestyle
have any information about, we set up a harsher testing strategy.
contains Public Libraries, Book Stores, and Public Arts. It also
We first randomly select non-zero rows instead of elements from
showsapreferenceforgyms(Gym,Stadiums,andBilliardParlor)
theoriginalmatrixtoformthetestingdata. Wethencomparethe
ofpeopleofthislifestyle.
non-zeroelementintheserowswiththepredictionresults. Welet
Lifestyle3isquitelife-oriented. Weobservetheshoppingpat-
n = 30,m = 40,andl = 10,thentrainthemodelson80%and
tern of Furniture, Home Textiles, and Large Home Appliances is
90%oftheoriginaldata,respectively. Weruntheexperimentten
listedinthesecondplaceinthislifestyle. Thispatternimpliesthe
times,andreporttheaverageresultsinTable1.
need for home arrangements and decorations, and it matches the
Thetableshowsthatourmodeloutperformsallbaselinemodels,
first mobility pattern of the mobility view, which is lead by Fur-
andbringsadramaticimprovementtothebasicmatrixfactorization
niture Stores. Also, auto accessories are of high demand in this
modelonbothmetrics.First,introducingmobilitypatternsintothe
lifestyle. This corresponds to the visits to garages and dealers in
taskwithaCMFmodelbringsalargeboosttoMF.Thisindicates
themobilityview.Moreover,thepeopleofthislifestylehavearel-
thattheconnectionbetweenhumanshoppingpatternsandmobil-
ativelyhighpreferencefortraveling,becausetheypaymorevisits
itypatternsworkseffectivelyforprediction. Ourmodellearnsthe
toscenicspotsandpublicsquares(secondmobilitypattern). This
connectionfromtheknownregions,andleveragesittopredictthe
mobility pattern, conversely, implies the demands of auto acces-
regionswhereshoppingpatternsareunknown. Addingneighbor-
soriesoftheshoppingview. Thetopshoppingpatternconsistsof
inginformationtothemodelalsoincreasesperformance. Because
computer accessories, cellphone accessories, and other computer
people prefer to move to a close place [23], a region usually af-
products,whichdecriesageneraldemandforelectricproducts.
fectstheneighboringregionsthemost. Furthermore,replacingthe
Lifestyle4hasthemobilitypatternsthatmainlyfocusonenter-
neighboringinformationwithinteractionsbetweenregionsbrings
tainment(Cinemas,VideoGameHalls,andBoardGames),shop-
anotherreasonableboosttoaccuracy. Thisprovesourassumption
pingplaces(Supermarkets,SportingStores,andConvenientStores),
thattheinteractionsbetweenregionsalsospreadtheshoppingpat-
andschoolsoruniversities(SchoolDormitories,SchoolLaborato-
terns of them. In the next section, we report our prediction re-
ries,andSchoolLibraries).Fromtheviewofmobility,wesuppose
sults,anddiscussthegeographicaldistributionsofdifferentshop-
thatmanystudentscouldhavehighratesonthislifestyle. Astoits
pingpatterns.
shoppingview,Men’sClothing,MaleShoes,andClothingAcces-
soriesformthetopshoppingpatternofthislifestyle,followedby
7. MULTIPLEVIEWSOFLIFESTYLES digital products, and then books. Women’s clothing is also quite
popularinthislifestyle,butnotlistedinthetopthree.Forstudents,
Inourresults,V(cid:62)andV(cid:62)indicateshoppingandmobilityviews
1 2 itisnaturaltohavehighdemandforbooks.Thisisalsoreflectedin
oflatentlifestyles. Thereasonablemappingsbetweentheseviews
ourpredictionresults–theleveloftheshoppingpatternofbookis
guarantee the effectiveness of incorporating mobility patterns to
highineducationalregions(seeSection8.3). Moreover,teenagers
predicttheshoppingpatterns. Inthissection,wediscussthemap-
usuallyaremoreinterestedindigitalproducts,whichexplainsthe
pings discovered in the results. Note that, a shopping view of
digitalproductsshoppingpattern.
lifestyle consists of a group of related shopping patterns, and a
The reasonable mapping between two views explains why our
shoppingpatternisformedbyagroupofrelatedproductcategories.
modelworkseffectively: aregionhascertainlatentlifestyles,and
Itisthesameformobilityviewsoflifestyle.Duetothelengthlimit,
the views of lifestyles are actually connected. Therefore, we are
we pick four latent lifestyles as examples. We list the top three
abletoutilizethedataofanotherview(mobility)topredictionour
shopping/mobilitypatternsforthelatentlifestylesinTable2. For
target(shopping).
eachshopping/mobilitypattern, welistthetopthreeproduct/POI
categories.
Lifestyle Lifestyle1 Lifestyle2
Views ShoppingView MobilityView ShoppingView MobilityView
WomenClothing Offices Tablets PublicLibraries
FirstPattern Underwear ConvenientStores PhotographyProducts BookStores
WomenShoes Commute DigitalAccessories PublicArt
Laptops Apartments&Communities Books Gymnasium&stadiums
SecondPattern OfficeAPLS&Stationery Commute Children’sBooks BilliardParlors
Tablets OtherPOIs Health&Fitness SchoolGyms
Cellphones ShoppingMalls PeripheralProducts DigitalApplianceStores
ThirdPattern CellphonePlans CosmeticsStores NetworkProducts Commute
CellphoneAccessories SportingStores Underwear SportingStores
Lifestyle Lifestyle3 Lifestyle4
Views ShoppingView MobilityView ShoppingView MobilityView
ComputerAccessories FurnitureStores MenClothing Cinemas
FirstPattern CellphoneAccessories CosmeticsStores MenShoes VideoGameHalls
OtherComputerProducts Garages&Dealers ClothingAccessories BoardGames
Furniture PublicSquares&Blocks Tablets Supermarkets
SecondPattern HomeTextiles ScenicSpots PhotographyProducts SportingStores
LargeHomeAppliances OtherPOIs DigitalAccessories ConvenientStores
AutoAccessories Cinema Books SchoolDormitories
ThirdPattern Multimediaproducts VideoGameHalls Children’sBooks SchoolLaboratories
CellphoneAccessories BoardGames Health&Fitness SchoolLibraries
Table2:Fourlifestylesfrombothshoppingandmobilityviews.Foreachviewwelistthetopfourmostweightedshopping/mobilitypatterns,
andforeachpatternwelistthetopfourmostweightedproduct/POIcategories.“APLS”isshortfor“Appliances”.
8. PREDICTED SHOPPING PATTERNS IN
BEIJING
Thegeographicaldistributionsofshoppingpatternsnotonlytell
thevariousdemandsofregions,butalsouncovertheurbanstruc-
ture of a city. For example, a high level of shopping behaviors
impliesahighlevelofcommerce. Furthermore,thelevelofaspe-
cificshoppingpatterncouldrevealkeyfeaturesofregions. Inthis
section, wedisplayourpredictionresultsonamap, andcompare
thelevelsoftheseshoppingpatternscity-wide. Cleardifferences
betweenthedistributionsrevealinterestinginsightstothecity. Figure5: Thegeographicaldistributionsoftwoshoppingpatterns
on a Beijing subway map. The top three weighted product cate-
8.1 BusinessCentersofBeijing
goriesofthemare: Laptops,OfficeAppliances&Stationery,and
WeplotthepredictionresultsoftwoshoppingpatternsonaBei- Tablets (left); Women Clothing, Underwear, and Women Shoes
jingsubwaymapinFigure5. Theshoppingpatternontheleftis (right).
mainlyaboutofficeappliances,andtheoneontherightismainly
about women’s clothing. The distributions of these two patterns onthelinematchwiththesubwaystationsofLine1.Thisconcurs
arequitesimilar,exceptthatthepatternofwomenclothingismore withtheconclusionsonthepositiveeffectsofsubwaystationson
widely spread. We suppose that this implies the online shopping theeconomiesoftheirsurroundings[8]. Also,itcanbeobserved
behaviorsofofficeclerksduringworkinghours. Asweshowedin that,thelineissplitbyTian’anmenSquareandtheForbiddenCity,
lastsection, officeappliancesandwomen’sclothingareclustered twolandmarksinBeijing.Thelevelsofthispatternintheeastpart
into the shopping view of the same latent lifestyle in our results onthislinearehigherthanthewest. Thisagreeswiththefactthat
(seeTable2,shoppingviewoflifestyle1). theeasternareasaremoredevelopedthanthewesternareasinBei-
Moreimportantly,thedistributionofoffice-relatedshoppingpat- jing.AccordingtothestatisticsfromtheBeijingMunicipalBureau
ternsimpliesthelocationsofbusinesscentersinthecity. Several ofStatistics4,thiseasterndistrict(Chaoyangdistrict)hasthehigh-
regions have much higher levels on this shopping pattern. Two estGrossDomesticProduct(GDP)amongalldistrictsinBeijing.
groups of regions can be observed on the map. The regions in Thesecondgroupconsistsoftheregionsinthenorthwesterncor-
thefirstgrouparelocatedinthemiddleofthecity, andtheyline ner of the city – Haidian district (circled on the map). This is a
uphorizontally. ThelinelinkingtheseregionsalignswithBeijing high-techdistricthostingmanysoftwareandcomputer-technology
SubwayLine1,whichishighlightedonthefigureswithadashline. companies,aswellasseveralhighereducationinstitutes5.Ourre-
Line1isnotonlytheoldest,butalsothemostheavilyusedlineof sultsdenotethatthisdistricthasgrowntoanotherbusinesscenter
theBeijingSubway3. Itlinkstheearliestandmostimportanteco- ofthecity. ThisagreeswiththefactthatHaidiantakesthesecond
nomiccentersinthecity,suchasGuomao,Xidan,andWangfujing. placeoftheGDPrankingofBeijing.
Moreover,thelocationsofthehighestlevelsofshoppingpatterns
4http://old.bjstats.gov.cn/esite/
3https://en.wikipedia.org/wiki/Beijing_Subway 5https://en.wikipedia.org/wiki/Haidian_District
Bycomparingourresultswithrealworldlocations,weshowthat
ourpredictionsuccessfullylocatesthebusinesscentersinanurban
area.
8.2 CentralVS.PeripheralRegions
AnothertwoshoppingpatternsarevisualizedinFigure6. The
oneontheleftisabouthousearrangementsanddecorations,since
Furniture,HomeTextiles,andLargeHomeAppliancesarethemost
weighted product categories of this pattern. The other shopping
patternontherightweightsBabyProducts, Toys, andChildren’s Figure7: Thegeographicaldistributionsoftwoshoppingpatterns.
Booksthemost.Notsurprisingly,thebusinesscentersthatwedis- Thetopthreeweightedproductcategoriesofthemare: Desktops,
cussedinlastsectionhavehighlevelsofthesepatterns. Because Computer Accessories, and Network Accessories (left); Books,
manypeopleworkintheseareas,thehighpopulationbaseofpo- Children’sBooks,andHealth&Fitness(right).
tentialonlineshoppingconsumersnaturallyresultsinahighlevel
ofdemand. Sincetheneedforcomputersareuniversalinmodernjobs, we
canstillobserveotherbusinesscentersinthemiddleofthecityin
thefigure. WhileanothershoppingpatternontherightofFigure7
hasthenarrowestgeographicaldistributionamongallofthem.There
areonlyafewregionshaverelativelyhighlevelsforthisshopping
pattern. Book is the dominant category in this pattern. Due to
thenaturaldemandsforbooksofstudentsandtechnicians,Haidian
districthasthehighestdemandsforbookscomparingwithotherre-
gions.RegionsA(circled)hasthelargestbookstoreinBeijing.We
supposethisisbecausepeoplewouldsearchbooksonlineforextra
informationsuchreviewswhenvisitingabookstore.RegionB(ci-
Figure6: Thegeographicaldistributionsoftwoshoppingpatterns. cled)istheCentralBusinessDistrict(CBD)ofBeijing. Itappears
Thetopthreeweightedproductcategoriesofthemare: Furniture, thatthisregionalsohasarelativelyhighdemandsforbooks.
HomeTextiles,andLargeHomeAppliances(left);BabyProducts,
Toys,andChildrenBooks(right). 9. CONCLUSIONANDFUTUREWORK
In this paper, we focus in predicting shopping patterns of dif-
Compared with the shopping patterns of office appliances and ferentcityregionsusingsparsedata. Weincorporatetwotypesof
women’sclothing(SeeFigure5),thesetwopatternsaremorewidely extrainformationtohelpsolvesuchachallengingproblem. First,
spread. Thelevelsofthepatternofhomeappliancesarerelatively we connect the shopping patterns with the mobility patterns in a
highintheperipheralareasofBeijing. Asforthepatternsofbaby region,inotherwords,use“wherepeoplego”tohelpinfer“what
products, there are two observations. First, demand for it covers peoplebuy”. Second,wemodeltheinteractionsbetweenregions,
theentirecity,whichimpliesthatthisisageneraldemandindaily and leverage the information of known regions to infer the shop-
life.Second,thelevelsintheperipheralregionsofthispatternalso ping patterns in unknown regions. A hybrid model is introduced
appearhigherthanthecentralareas(exceptforthebusinesscen- forthepredictiontask. Theexperimentresultsindicatethatincor-
ters).Thedifferencebetweencentralandperipheralareasreflected porating extra information in the task leads to a significant boost
by our results can be explained by the process of urban develop- (5%+intermsofRME,and10%+intermsofMEA)ofaccuracy
ment of Beijing. The central areas of Beijing are traditional dis- for our prediction task. More importantly, our prediction results
tricts. These districts are well developed, and the average age of revealkeyfunctionalcharacteristicsofurbanareas, includingthe
theresidentsintheseareasarerelativelyhigh. Whiletheperiph- locations of business centers, the difference between central and
eralregionsarestillunderdeveloping.Youngerpeopletendtolive peripheralareas,andtheuniquedemandsofcertaincityregions.
intheseareas. Also,manyongoingrealestateprojectsarelocated Therearemainlytwodirectionsforourfuturework. First, we
there. Sinceproductsforhousearrangementsanddecorationsare wouldliketointroducethetemporaldimensionintotheshopping
usuallyfornewhouses,itisreasonabletoobservehighlevelsinpe- patternpredictiontask. Sincepeople’sdemandsvaryalongtime,
ripherals. Meanwhile,thebabyproductsareusuallypurchasedby itismorevaluabletopredict“whatpeoplebuyatwhen”. Second,
youngparents,andthepeoplelivinginperipheralregionsarerel- wewouldliketointroducemoreviewsoflifestyles, suchasper-
ativelyyoung,whichexplainsthehigherlevelofthebabyproduct sonalities, occupations, and so on, to our framework. There are
patternintheseregions. manyviewstoobservehowpeoplelive. Itisthereforebeneficial
to link more views, and obtain a better and more comprehensive
8.3 UniqueShoppingPatternsofRegions
understandingofhumanlifestyles.
Itisnotalwaystruethatbusinesscentershavethehighestlevels
ofallshoppingpatterns. Weobservesomeshoppingpatternsthat 10. ACKNOWLEDGMENT
areonlypredictedpopularincertainregions. TheleftplotofFig-
We would like to thank the Xerox Foundation. We also thank
ure7describesthegeographicaldistributionofthegroupforDesk-
XitongYangforhelpingwiththeplotsinthispaper.
tops,ComputerAccessories,andNetworkAccessories. Although
this shopping pattern is quite work-related, Haidian district has a
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