Big Data Recommender Systems: Application Paradigms (Computing and Networks)

520 Pages·2019·16.132 MB·English

by  Osman Khalid (editor), Samee U. Khan (editor), Albert Y. Zomaya (editor)

IETPROFESSIONALAPPLICATIONSOFCOMPUTINGSERIES35
Big Data Recommender
Systems
IETBookSeriesonBigData–CallforAuthors
Editor-in-Chief:ProfessorAlbertY.Zomaya,UniversityofSydney,Australia
ThetopicofBigDatahasemergedasarevolutionarythemethatcutsacross
many technologies and application domains. This new book series brings
together topics within the myriad research activities in many areas that
analyse, compute, store, manage and transport massive amount of data,
such as algorithm design, data mining and search, processor architectures,
databases,infrastructuredevelopment,serviceanddatadiscovery,network-
ingandmobilecomputing,cloudcomputing,high-performancecomputing,
privacyandsecurity,storageandvisualization.
Topicsconsideredinclude(butnotrestrictedto)IoTandInternetcomputing;
cloudcomputing;peer-to-peercomputing;autonomiccomputing;datacen-
tre computing; multi-core and many core computing; parallel, distributed
and high-performance computing; scalable databases; mobile computing
and sensor networking; green computing; service computing; networking
infrastructures; cyberinfrastructures; e-Science; smart cities; analytics and
datamining;BigDataapplicationsandmore.
Proposals for coherently integrated International co-edited or co-authored
handbooksandresearchmonographswillbeconsideredforthisbookseries.
Eachproposalwillbereviewedbytheeditor-in-chiefandsomeboardmem-
bers, with additional external reviews from independent reviewers. Please
email your book proposal for the IET Book Series on Big Data to: Pro-
fessor Albert Y. Zomaya at [email protected] or to the IET at
[email protected].
Othervolumesinthisseries:
Volume1 KnowledgeDiscoveryandDataMiningM.A.Bramer(Editor)
Volume3 TroubledITProjects:PreventionandturnaroundJ.M.Smith
Volume4 UMLforSystemsEngineering:Watchingthewheels,2ndEditionJ.Holt
Volume5 IntelligentDistributedVideoSurveillanceSystemsS.A.Velastinand
P.Remagnino(Editors)
Volume6 TrustedComputingC.Mitchell(Editor)
Volume7 SysMLforSystemsEngineeringJ.HoltandS.Perry
Volume8 ModellingEnterpriseArchitecturesJ.HoltandS.Perry
Volume9 Model-basedRequirementsEngineeringJ.Holt,S.PerryandM.Bownsword
Volume13 TrustedPlatformModules:Why,whenandhowtousethemArielSegall
Volume14 FoundationsforModel-basedSystemsEngineering:Frompatternstomodels
J.Holt,S.PerryandM.Bownsword
Volume15 BigDataandSoftwareDefinedNetworksJ.Taheri(Editor)
Volume18 ModelingandSimulationofComplexCommunicationM.A.Niazi(Editor)
Volume20SysMLforSystemsEngineering:Amodel-basedapproach,3rdEditionJ.Holt
andS.Perry
Volume23DataasInfrastructureforSmartCitiesL.SuzukiandA.Finkelstein
Volume24UltrascaleComputingSystemsJ.Carretero,E.JeannotandA.Zomaya
Big Data Recommender
Systems
Volume 2: Application Paradigms
Edited by
Osman Khalid, Samee U. Khan and Albert Y. Zomaya
The Institution of Engineering and Technology
PublishedbyTheInstitutionofEngineeringandTechnology,London,UnitedKingdom
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Firstpublished2019
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ISBN978-1-78561-977-9(hardbackVolume2)
ISBN:978-1-78561-975-5(hardbackVolume1)
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Contents
Foreword xix
1 Introductiontobigdatarecommendersystems—volume2 1
OsmanKhalid,SameeU.Khan,andAlbertY.Zomaya
1.1 Background 1
1.2 Aboutthebook 3
Acknowledgments 6
References 7
2 Deepneuralnetworksmeetrecommendersystems 9
ShuaiZhang,LinaYao,AixinSun,GuibingGuo,XiweiXu,
andLimingZhu
2.1 Preliminary 10
2.1.1 Introductiontorecommendersystems 10
2.1.2 Introductiontodeepneuralnetworks 10
2.2 Introducingnonlinearitytorecommendersystems 12
2.2.1 Deepneuralgeneralizationofcollaborativefiltering 13
2.2.2 Deepneuralgeneralizationoffactorizationmachine 16
2.3 Representationlearningforrecommendersystems 17
2.3.1 Representationlearningwithmultilayerperceptron 17
2.3.2 Representationlearningwithautoencoder 18
2.3.3 Representationlearningwithconvolutional
neuralnetwork 18
2.3.4 RepresentationlearningwithWord2Vec 20
2.4 Sequencemodellingforrecommendersystems 21
2.4.1 Session-basedrecommendations 21
2.4.2 Sequence-awarerecommendersystems 23
2.5 Deephybridmodelsforrecommendersystems 24
2.6 Advancedtopics 25
2.6.1 Metriclearning 25
2.6.2 Generativeadversarialnetworks 26
2.6.3 Neuralautoregressivedistributionestimator 27
2.7 Futurechallengesandconclusion 27
References 28
viii Bigdatarecommendersystems,volume2
3 Cold-startsolutionsforrecommendationsystems 35
FarshadBakhshandeganMoghaddamandMehdiElahi
3.1 Introduction 35
3.1.1 Recommendationapproaches 36
3.2 Collaborativefiltering 37
3.3 Activelearninginrecommendersystems 38
3.4 Semantic-basedrecommendersystems 40
3.5 Recommendationbasedonvisualfeatures 42
3.6 Personality-basedrecommendersystems 43
3.7 Cross-domainrecommendersystems 45
3.8 Conclusion 46
References 47
4 Performancemetricsfortraditionalandcontext-awarebigdata
recommendersystems 57
RabNawazJadoon,WuYang,andOsmanKhalid
4.1 Introduction 58
4.2 CARS—abriefoverview 59
4.3 EvaluationofRSs 59
4.3.1 Evaluationmetrics 60
4.4 DiversityandaccuracymetricsusedinCARS 66
4.4.1 HowrecommendationaccuracyismeasuredinCARS? 66
4.4.2 DiversitymeasurementinCARS 66
4.5 Howtochooseanappropriateevaluationmetrics? 67
4.6 Conclusion 68
Acknowledgments 68
References 68
5 Miningurbanlifestyles:urbancomputing,humanbehavior
andrecommendersystems 71
SharonXu,RiccardoDiClemente,andMartaC.González
5.1 Miningshoppingandmobilitypatterns 72
5.1.1 Predictionofshoppingbehaviorwithdatasparsity 72
5.1.2 Addingcontextualinformationtolocationdata 72
5.1.3 Multi-perspectivelifestyles 73
5.2 Data 73
5.3 Discoveringshoppingpatterns 74
5.4 Mobilitypatternextraction 75
5.4.1 Extractingcellulartowerlocationtypes 75
5.4.2 Baselinemethods 77
5.4.3 Characterizingmobilitypatterns 77
5.5 Predictingshoppingbehavior 77
5.5.1 Collectivematrixfactorization 77
Contents ix
5.6 Results 78
5.6.1 Prediction 78
5.6.2 Duallifestyles 78
5.7 Discussion 79
Acknowledgments 79
References 79
6 Embeddingprincipalcomponentanalysisinferenceinexpert
sensorsforbigdataapplications 83
RodrigoMarino,JoseM.Lanza-Gutierrez,andTeresaRiesgo
6.1 Introduction 83
6.2 Relatedwork 86
6.3 Principalcomponentanalysis:problemformulation 87
6.4 Workflowdescription 88
6.5 Embeddedarchitecture 89
6.5.1 System-levelarchitecture 89
6.5.2 PCAinferenceIPdescription 91
6.6 Experimentalmethodology 93
6.7 Experimentalresults 94
6.7.1 8-vs.16-bitarchitectures 95
6.7.2 Hardwarearchitecturevs.multicoreapproach 100
6.8 Conclusions 102
Acknowledgments 102
References 102
7 Decisionsupportsystemtodetecthiddenpathologiesofstroke:
theCIPHERproject 107
JoséGonzálezEnríquez,LeticiaMoralesTrujillo,
SaraMorenoLeonardo,FranciscoJoséDomínguezMayo,
JuliánAlbertoGarcíaGarcía,andManuelMejíasRisoto
7.1 Introduction 107
7.2 Context:theCIPHERproject 109
7.3 Decisionsupportsystem 112
7.4 Validation 114
7.4.1 Dataprocessing 114
7.4.2 Algorithmselection 118
7.4.3 Firstresults 119
7.5 Conclusionsandfutureworks 121
Acknowledgments 122
References 122
8 Bigdataanalyticsforsmartgrids 125
PanagiotisD.DiamantoulakisandGeorgeK.Karagiannidis
8.1 Introduction 125