Algorithms and Applications for Academic Search, Recommendation and Quantitative Association Rule Mining

132 Pages·2022·15.751 MB·English

by  Emmanouil Amolochitis

Algorithms and Applications
for Academic Search, Recommendation
and Quantitative Association Rule Mining
RIVER PUBLISHERS SERIES IN AUTOMATION,
CONTROL AND ROBOTICS
SeriesEditors
SRIKANTAPATNAIK ISHWARK.SETHI
SOAUniversity OaklandUniversity
Bhubaneswar,India USA
QUANMINZHU
UniversityoftheWestofEngland
UK
Advisor
TAREKSOBH
UniversityofBridgeport
USA
Indexing: All books published in this series are submitted to the Web of Science
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The “River Publishers Series in Automation, Control and Robotics” is a series of
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The NEC and You Perfect Together:
Algorithms and Applications
fAo rCAomcapdreehmeincsSiveea Srctuhd, yR eocf othme mendation
and QuaNnatittiaotnivael EAlescstoricciaalt CioondRe ule Mining
Gregory P. Bierals
Emmanouil Amolochitis
Electrical Design Institute, USA
UniversityofAalborg
Denmark
River Publishers
Published2018byRiverPublishers
RiverPublishers
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Algorithms and Applications for Academic Search, Recommendation and
QuantitativeAssociationRuleMining/byEmmanouilAmolochitis.
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Contents
Abstract ix
Acknowledgements xi
ListofFigures xiii
ListofTables xvii
1 1
1.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . 1
1.2 AlgorithmicMotivationandObjectives . . . . . . . . . . . 3
1.3 RelatedWork . . . . . . . . . . . . . . . . . . . . . . . . . 5
1.4 AlgorithmicChallenges . . . . . . . . . . . . . . . . . . . 11
2 AcademicSearchAlgorithms 17
2.1 CollectingDatafromScientificPublications . . . . . . . . . 17
2.2 TopicSimilarityUsingGraphs . . . . . . . . . . . . . . . . 18
2.2.1 GraphConstruction . . . . . . . . . . . . . . . . . . 18
2.2.2 TypeIGraph . . . . . . . . . . . . . . . . . . . . . 18
2.2.3 TypeIIGraph . . . . . . . . . . . . . . . . . . . . . 19
2.3 TopicSimilarityUsingGraphs . . . . . . . . . . . . . . . . 20
2.4 SystemArchitecture . . . . . . . . . . . . . . . . . . . . . 20
2.5 HeuristicHierarchy . . . . . . . . . . . . . . . . . . . . . . 21
2.5.1 TermFrequencyHeuristic . . . . . . . . . . . . . . 22
2.5.2 DepreciatedCitationCountHeuristic . . . . . . . . 26
2.5.3 MaximalWeightedCliquesHeuristic . . . . . . . . 28
2.6 Experiments’Design . . . . . . . . . . . . . . . . . . . . . 29
2.7 ExperimentalResults . . . . . . . . . . . . . . . . . . . . . 32
2.7.1 ComparisonswithACMPortal . . . . . . . . . . . . 33
2.7.2 ComparisonwithotherHeuristicConfigurations . . 34
v
vi Contents
2.7.3 ComparisonwithOtherAcademicSearch
Engines . . . . . . . . . . . . . . . . . . . . . . . . 43
2.7.4 CanPubSearchPromoteGoodPublications
“Buried”inACMPortalResults? . . . . . . . . . . 49
2.7.5 Run-timeOverhead . . . . . . . . . . . . . . . . . . 52
3 RecommenderSystems 53
3.1 SystemArchitectureOverview . . . . . . . . . . . . . . . . 53
3.1.1 AMOREWebService . . . . . . . . . . . . . . . . 54
3.1.2 AMOREBatchProcess. . . . . . . . . . . . . . . . 55
3.2 RecommenderEnsemble . . . . . . . . . . . . . . . . . . . 60
3.2.1 RecommendationApproach . . . . . . . . . . . . . 60
3.2.2 Content-BasedRecommender . . . . . . . . . . . . 60
3.2.3 Item-BasedRecommender . . . . . . . . . . . . . . 62
3.2.4 User-BasedRecommender . . . . . . . . . . . . . . 64
3.2.5 FinalHybridParallelRecommenderEnsemble . . . 64
3.2.6 ExperimentswithOtherBaseRecommender
Algorithms . . . . . . . . . . . . . . . . . . . . . . 65
3.3 ComputationalResults . . . . . . . . . . . . . . . . . . . . 65
3.4 UserandSystemInterfaces . . . . . . . . . . . . . . . . . . 74
4 QuantitativeAssociationRulesMining 77
4.1 WhyQuantitativeAssociationRules? . . . . . . . . . . . . 77
4.2 AlgorithmOverview . . . . . . . . . . . . . . . . . . . . . 78
4.3 AlgorithmDesign . . . . . . . . . . . . . . . . . . . . . . 79
4.4 RecommenderPost-Processor . . . . . . . . . . . . . . . . 82
4.4.1 Overview . . . . . . . . . . . . . . . . . . . . . . . 82
4.4.2 Post-ProcessingAlgorithm . . . . . . . . . . . . . . 82
4.5 SyntheticDatasetGenerator . . . . . . . . . . . . . . . . . 83
4.6 ConfigurationParameters. . . . . . . . . . . . . . . . . . . 84
4.7 ItemDemandElasticity. . . . . . . . . . . . . . . . . . . . 84
4.8 DatasetGenerationProcess. . . . . . . . . . . . . . . . . . 84
4.8.1 GenerationCycle . . . . . . . . . . . . . . . . . . . 86
4.8.2 UpdateCycle . . . . . . . . . . . . . . . . . . . . . 87
4.9 ExperimentalResults . . . . . . . . . . . . . . . . . . . . . 88
4.9.1 Metric . . . . . . . . . . . . . . . . . . . . . . . . . 88
4.9.2 QARMResultsUsingSyntheticallyGenerated
Datasets . . . . . . . . . . . . . . . . . . . . . . . . 88
Contents vii
4.9.3 QARMResultsUsingMovielensDataset . . . . . . 95
4.9.4 QARMResultsUsingPost-Processor . . . . . . . . 95
5 ConclusionsandFutureDirections 101
References 105
Index 111
AbouttheAuthor 113

Abstract
Inthebook,wepresentnovelalgorithmsforacademicsearch,recommenda-
tion,andassociationruleminingthathavebeendevelopedandoptimizedfor
different commercial as well as academic purpose systems. Along with the
designandimplementationofalgorithms,amajorpartoftheworkpresented
inthebookinvolvesthedevelopmentofnewsystemsbothforcommercialas
well as for academic use. In the first part of the book, we introduce a novel
hierarchical heuristic scheme for re-ranking academic publications retrieved
from standard digital libraries. The scheme is based on the hierarchical
combination of a custom implementation of the term frequency heuristic,
a time-depreciated citation score, and a graph-theoretic computed score that
relatesthepaper’sindextermswitheachother.Inordertoevaluatetheperfor-
manceoftheintroducedalgorithms,ameta-searchenginehasbeendesigned
and developed that submits user queries to standard digital repositories of
academic publications and re-ranks the top-n results using the introduced
hierarchical heuristic scheme. On the second part of the book, we describe
thedesignofnovelrecommendationalgorithmswithapplicationindifferent
types of e-commerce systems. The newly introduced algorithms are a part
of a developed Movie Recommendation system, the first such system to be
commercially deployed in Greece by a major Triple Play services’ provider.
The initial version of the system uses a novel hybrid recommender (user,
item, and content based) and provides daily recommendations to all active
subscribersoftheprovider(currentlymorethan30,000).Therecommenders
thatwearepresentingarehybridbynature,usinganensembleconfiguration
of different content, user, as well as item-based recommenders in order to
providemoreaccuraterecommendationresults.Inthethirdpartofthebook,
we present the design of a quantitative association rule mining algorithm.
Quantitative association rules refer to a special type of association rules of
the form: antecedent implies consequent consisting of a set of numerical or
quantitativeattributes.Theintroducedminingalgorithmprocessesaspecific
numberofuserhistoriesinordertogenerateasetofassociationruleswitha
minimally required support and confidence value. The generated rules show
ix