Neutrosophic Sets and Systems, Vol. 8, 2015 M.M.Lotfy , Review of Recommender Systems Algorithms Utilized in Social Networks based e-Learning Systems & Neutrosophic SystemReview of Recommender Systems Algorithms Utilized in Social Networks based e-Learning Systems & Neutrosophic SystemA. A. Salama1, Mohamed Eisa2, S.A.ELhafeez3 and M. M. Lotfy41,3,4 1 Department ofMathematics and Computer Science, Faculty of Sciences, Port Said University, 23 December Street, Port Said 42522, Egypt.Email: drsalama44@gmail.com 2Computer Science Department, Port Said University, 42526 Port Said, Egypt. Email: mmmeisa@yahoo.com Abstract.Inthispaper,wepresentareviewofdifferent recommendersystemalgorithmsthatareutilizedinsocial networks based e-Learning systems. Future research will include ourproposedoure-LearningsystemthatutilizesRecommender SystemandSocialNetwork.Sincetheworldisfullof indeterminacy,theneutrosophicsfoundtheirplaceinto contemporaryresearch.Thefundamentalconceptsof neutrosophic set, introduced by Smarandache in [21, 22, 23] and Salamaetal.in[24-66].Thepurposeofthispaperistoutilizea neutrosophicsettoanalyzesocialnetworksdataconducted through learning activities. Keywords: e-Learning , Social Networks , Recommender System , Neutrosophic System.1 Introduction TheInternetshowsgreatpotentialforenhancing collaborationbetweenpeopleandtheroleofsocial software has become increasingly relevant in recent years. Avastarrayofsystemsexistwhichemployusersstored profiledata,identifyingmatchesforcollaboration.Social interaction within an online framework can help university studentsshareexperiencesandcollaborateonrelevant topics.Assuch,socialnetworkscanactasapedagogical agent, for example, with problem-based learning [1].Social networkingwebsitesarevirtualcommunitieswhichallow peopletoconnectandinteractwitheachotherona particularsubjectortojusthangouttogetheronline. Membershipofonlinesocialnetworkshasrecently exploded at an exponential rate [2]. Recommender systems coveranimportantfieldwithincollaborativeservicesthat aredevelopedintheWeb2.0environmentandenable user-generatedopinionstobeexploitedinasophisticated andpowerfulway.RecommenderSystemscanbe considered as social networking tools that provide dynamic andcollaborativecommunication,interactionand knowledge [3]. Coursemanagementsystems(CMSs)canofferagreat varietyofchannelsandworkspacestofacilitate informationsharingandcommunicationamong participantsinacourse.Theyleteducatorsdistribute informationtostudents,producecontentmaterial,prepare assignmentsandtests,engageindiscussions,manage distanceclassesandenablecollaborativelearningwith forums,chats,filestorageareas,newsservices,etc.Some examplesofcommercialsystemsareBlackboard,WebCT andTopClasswhilesomeexamplesoffreesystemsare Moodle,IliasandClaroline.Nowadays,oneofthemost commonlyusedisMoodle(modularobjectoriented developmentallearningenvironment),afreelearning managementsystemenablingthecreationofpowerful, flexibleandengagingonlinecoursesandexperiences [4,30]. Theneweraofe-Learningservicesismainlybasedon ubiquitouslearning,mobiletechnologies,socialnetworks (communities)andpersonalizedknowledgemanagement. Theconvergenceofe-Learningandknowledge managementfostersaconstructive,open,dynamic, interconnected, distributed, adaptive, user friendly, socially concerned,andaccessiblewealthofknowledge.The 32Neutrosophic Sets and Systems, Vol. 8, 2015 M.M.Lotfy , Review of Recommender Systems Algorithms Utilized in Social Networks based e-Learning Systems & Neutrosophic Systemknowledgemanagementtoolssuchascommunity,social software,peer-to-peerandpersonalizedknowledge managementandarenowcommonlyarebeingusedin ubiquitouslearning.Learnersusethesetoolstogenerate andshareideas,exploretheirthinking,andacquire knowledgefromotherlearners.Learnerssearchand navigatethelearningobjectsinthisknowledgefilled environment.However,duetothefailureofindexing methodstoprovidetheanticipated,ubiquitouslearning gridforthem,learnersoftenfailtoreachtheirdesired learningobjects[5].Thefundamentalconceptsof neutrosophicset,introducedbySmarandache[21,22,23] and Salama et al. in [24-66], provides a natural foundation for treating mathematically the neutrosophicphenomenawhichexistpervasivelyinour realworldandforbuildingnewbranchesofneutrosophic mathematics and computer applications. This paper goes as follows: Section Two presents different RecommenderSystemsalgorithmsthatcanbeutilizedin e-Learning.SectionthreepresentstheC4.5algorithm. SectionfourpresentstheK-meansalgorithm.Sectionfive introduces the Support Vector Machines algorithm. Section six highlights the Apriori algorithm. Section seven presents the conclusion and future work.the notion of neutrosophics crisp set.2 Recommender Systems ThereisaneedforPersonalRecommenderSystemsin Learning Networks in order to provide learners with advice onthesuitablelearningactivitiestofollow.Learning Networks target lifelong learners in any learning situation, atalleducationallevelsandinallnationalcontexts.They arecommunity-drivenbecauseeverymemberisableto contribute to the learning material. Existing Recommender Systemsandrecommendationtechniquesusedfor consumer products and other contexts are assessed on their suitabilityforprovidingnavigationalsupportinaLearner Networks..3 C4.5 Systemsthatconstructclassifiersareoneofthe commonly used tools in data mining. Such systems take as inputacollectionofcases,eachbelongingtooneofa smallnumberofclassesanddescribedbyitsvaluesfora fixedsetofattributes,andoutputaclassifierthatcan accuratelypredicttheclasstowhichanewcase belongs.LikeCLSandID3,C4.5generatesclassifiers expressedasdecisiontrees,butitcanalsoconstruct classifiers in more comprehensible ruleset form. A.Decision Trees Givena set S of cases, C4.5 first grows an initial treeusingthedivide-and-conqueralgorithms follows: - If all the cases in S belong to the same class or Sissmall,thetreeisaleaflabeledwiththemostfrequent class in S.- Otherwise,chooseatestbasedonasingleattributewithtwoormoreoutcomes.Makethistesttherootofthetreewithonebranchforeachoutcome of the test, partition S into correspondingsubsetsaccordingtothe outcomeforeachcase,and apply the same procedure recursively to eachsubset.There are usually many tests that could be chosen in this last step. C4.5 uses two heuristic criteria to rankpossibletests:informationgain,which minimizes the total entropy of the subsets (but is heavilybiasedtowardstestswithnumerous outcomes),andthedefaultgainratiothatdivides informationgainbytheinformationprovidedby the test outcomes. B.Ruleset Classifier Complexdecisiontreescanbedifficulttounderstand, for instance because information about one class is usually distributed throughout the tree. C4.5 introduced an alterna-tive formalism consisting of a list of rules of the form if A andBandCand...thenclassX,whererulesforeach classaregroupedtogether.Acaseisclassifiedbyfinding thefirstrulewhoseconditionsaresatisfiedbythecase;if noruleissatisfied,thecaseisassignedtoadefault class.C4.5rulesetsareformedfromtheinitial(unpruned) decisiontree. Eachpathfromtheroot ofthetreetoaleaf becomesaprototyperulewhoseconditionsaretheout-comesalongthepathandwhoseclassisthelabelofthe leaf.Thisrule isthensimplifiedby determiningtheeffect of discarding each condition in turn. Dropping a condition mayincreasethenumberNofcasescoveredbytherule, andalsothenumberEofcasesthatdonotbelongtothe class nominated by the rule, and may lower the pessimistic errorratedeterminedasabove.Ahill-climbingalgorithm is used to drop conditions until the lowest pessimistic error rateisfound.Tocompletetheprocess,asubsetofsimpli-fiedrulesisselectedforeachclassinturn.Theseclass subsetsareorderedtominimizetheerroronthetraining cases and a default class is chosen. The final ruleset usual-lyhasfarfewerrulesthanthenumberofleavesonthe pruneddecisiontree.TheprincipaldisadvantageofC4.5s rulesetsistheamountofCPUtimeandmemorythatthey require. 4 K-Means Algorithm 33Neutrosophic Sets and Systems, Vol. 8, 2015 M.M.Lotfy , Review of Recommender Systems Algorithms Utilized in Social Networks based e-Learning Systems & Neutrosophic SystemThek-meansalgorithmisasimpleiterativemethodto partitionagivendatasetintoauserspecifiednumberof clusters,k.Thisalgorithmhasbeendiscoveredbyseveral researchers across different disciplines, most notably Lloyd [6],Forgey,FriedmanandRubin,andMcQueen.A detailedhistoryofk-meansalongwithdescriptionsof severalvariationsaregivenin[7].GrayandNeuhoff[8] provide a nice historical background for k-means placed in thelargercontextofhill-climbingalgorithms.The algorithm is initialized by picking k points in as the initial kclusterrepresentativesorcentroids.Techniquesfor selectingtheseinitialseedsincludesamplingatrandom from the dataset, setting them as the solution of clustering a small subset of the data or perturbing the global mean of thedataktimes.Thenthealgorithmiteratesbetweentwo steps till convergence: - Step1:DataAssignment.Eachdatapointisassignedtoitsclosestcentroid,withtiesbrokenarbitrarily.Thisresultsinapartitioningofthedata.- Step2:Relocationofmeans.Eachclusterrepresentative is relocated to the center (mean) ofalldatapointsassignedtoit.Ifthedatapointscomewithaprobabilitymeasure(weights),thentherelocationistotheexpectations(weightedmean) of the data partitions.The algorithm converges when the assignments (and hence the values) no longer change.One issue to resolve is how toquantifyclosestintheassignmentstep.Thedefault measureofclosenessistheEuclideandistance,inwhich caseonecanreadilyshowthatthenon-negativecost function =|||.|

\|nijj ic x122min arg,willdecreasewheneverthereisachangeinthe assignment or the relocation steps, and hence convergence isguaranteedinafinite number ofiterations.Thegreedy-descentnatureofk-meansonanon-convexcostalso impliesthattheconvergenceisonlytoalocaloptimum, andindeed thealgorithmistypicallyquite sensitiveto the initial centroid locations. A.Limitations Inadditionto beingsensitivetoinitialization,the k-meansalgorithmsuffersfromseveralother problems. First, observe that k-means is a limiting caseoffittingdatabyamixtureofkGaussians withidentical,isotropiccovariancematrices, whenthesoftassignmentsofdatapointsto mixture components are hardened to allocate each data point solely to the most likely component. So, itwillfalterwheneverthedataisnotwell described by reasonably separated spherical balls, forexample,iftherearenon-covexshaped clustersinthedata.Thisproblemmaybe alleviatedbyrescalingthedatatowhitenit beforeclustering,or byusinga differentdistance measurethatismoreappropriateforthedataset. Forexample,information-theoreticclustering usestheKL-divergencetomeasurethedistance between two data points representing two discrete probabilitydistributions.Ithasbeenrecently shownthatifonemeasuresdistancebyselecting anymemberofaverylargeclassofdivergences calledBregmandivergencesduringthe assignmentstepandmakesnootherchanges,the essentialpropertiesofk-means,including guaranteedconvergence,linearseparation boundariesandscalability,areretained[9].This resultmakesk-meanseffectiveforamuchlarger classofdatasetssolongasanappropriate divergence is used. k-meanscanbepairedwithanotheralgorithmtode-scribe non-convexclusters. Onefirstclustersthe datainto a large number of groups using k-means. These groups are then agglomerated into larger clusters using single link hi-erarchicalclustering,whichcandetectcomplexshapes. This approach also makes the solution less sensitive to ini-tialization,andsincethehierarchicalmethodprovidesre-sultsatmultipleresolutions,onedoesnotneedtopre-specify keither.The cost of the optimal solution decreases with increasing k till it hits zero when the number of clus-tersequalsthenumberofdistinctdata-points.Thismakes it more difficult to (a) directly compare solutions with dif-ferent numbers of clusters and (b) to find the optimum val-ue of k. If the desired k is not known in advance, one will typicallyrunk-meanswithdifferentvaluesofk,andthen use a suitable criterion to select one of the results. For ex-ample,SASusesthecube-clustering-criterion,whileX-means adds a complexity term (which increases with k) to theoriginalcostfunction(Eq.1)andthenidentifiesthek which minimizes this adjusted cost. Alternatively, one can progressivelyincreasethenumberofclusters,inconjunc-tionwithasuitablestoppingcriterion.Bisectingk-means [10] achieves this by first putting all the data into a single cluster,andthenrecursivelysplittingtheleastcompact cluster into two using 2-means. The celebrated LBG algo-rithm[8]usedforvectorquantizationdoublesthenumber ofclusterstillasuitablecode-booksizeisobtained.Both these approaches thus alleviate the need to know k before-34Neutrosophic Sets and Systems, Vol. 8, 2015 M.M.Lotfy , Review of Recommender Systems Algorithms Utilized in Social Networks based e-Learning Systems & Neutrosophic Systemhand.The algorithm is also sensitive to the presence of out-liers, since mean is not a robust statistic. A preprocessing step to remove outliers can be helpful. Post-processing the results, for example to eliminate small clusters, or to merge close clusters into a large cluster, is also desirable. Ball and HallsISODATAalgorithmfrom1967effectivelyused both pre- and post-processing on k-means.5 Support Vector Machines Intodaysmachinelearningapplications,support vectormachines(SVM)[11]areconsideredamusttryit offers one of the most robust and accurate methods among allwell-knownalgorithms.Ithasasoundtheoretical foundation,requiresonlyadozenexamplesfortraining, and is insensitive to the number of dimensions. In addition, efficientmethodsfortrainingSVMarealsobeing developedatafastpace.Inatwo-classlearningtask,the aimofSVMistofindthebestclassificationfunctionto distinguishbetweenmembersofthetwoclassesinthe trainingdata.Themetricfortheconceptofthebest classification function can be realized geometrically.Becausetherearemanysuchlinearhyperplanes,what SVMadditionallyguaranteeisthatthebestsuchfunction isfoundbymaximizingthemarginbetweenthetwo classes. Intuitively, the margin is defined as the amount of space, or separation between the two classes as defined by thehyperplane.Geometrically,themargincorrespondsto theshortestdistancebetweentheclosestdatapointstoa pointonthehyperplane.Havingthisgeometricdefinition allowsustoexplorehowtomaximizethemargin,sothat eventhoughthereareaninfinitenumberofhyperplanes, only a few qualify as the solution to SVM.The reason why SVMinsistsonfindingthemaximummarginhyperplanes is that it offers the best generalization ability. It allows not only the best classification performance (e.g., accuracy) on the training data, but also leaves much room for the correct classification of the future data.Thereareseveralimportantquestionsandrelated extensionsontheabovebasicformulationofsupport vectormachines.SVMcanbeeasilyextendedtoperform numericalcalculations.ThefirstistoextendSVMto perform regression analysis, where the goal is to produce a linearfunctionthatcanapproximatethattargetfunction. Carefulconsiderationgoesintothechoiceoftheerror models;insupportvectorregression,orSVR,theerroris defined to be zero when the difference between actual and predictedvaluesiswithinanepsilonamount.Otherwise, the epsilon insensitive error will grow linearly. The support vectorscanthenbelearnedthroughtheminimizationof the Lagrangian. An advantage of support vector regression is reported to be its insensitivity to outliers. Anotherextensionistolearntorankelementsrather than producing a classification for individual elements [12]. Rankingcanbereducedtocomparingpairsofinstances andproducinga+1estimateifthepairisinthecorrect ranking order, and 1 otherwise. Thus, a way to reduce this task to SVM learning is to construct new instances for each pairofrankedinstanceinthetrainingdata,andtolearna hyperplaneonthisnewtrainingdata.Thismethodcanbe applied to many areas where ranking is important, such as in document ranking in information retrieval areas. 6 The Apriori algorithm One of the most popular data mining approaches is to find frequentitemsetsfromatransactiondatasetandderive associationrules.Findingfrequentitemsets(itemsetswith frequency larger than or equal to a user specified minimum support)isnottrivialbecauseofitscombinatorial explosion.Oncefrequentitemsetsareobtained,itis straightforwardtogenerateassociationruleswith confidencelargerthanorequaltoauserspecified minimumconfidence.Aprioriisaseminalalgorithmfor findingfrequentitemsetsusingcandidategeneration[13]. Itischaracterizedasalevel-wisecompletesearch algorithmusinganti-monotonicityofitemsets,ifan itemsetisnotfrequent,anyofitssupersetisnever frequent.Byconvention,Aprioriassumesthatitems withinatransactionoritemsetaresortedinlexicographic order.Apriorifirstscansthedatabaseandsearchesfor frequentitemsetsofsize1byaccumulatingthecountfor eachitemandcollectingthosethatsatisfytheminimum support requirement. It then iterates on the following three steps and extracts all the frequent itemsets. Manyofthepatternfindingalgorithmssuchasdecision tree,classificationrulesandclusteringtechniquesthatare frequentlyusedindatamininghavebeendevelopedin machinelearningresearchcommunity.Frequentpattern and association rule mining is one of the few exceptions to thistradition.Theintroductionofthistechniqueboosted dataminingresearchanditsimpactistremendous.The algorithmisquitesimpleandeasytoimplement. Experimenting with Apriori-like algorithm is the first thing that data miners try to do. SinceApriorialgorithmwasfirstintroducedandas experiencewasaccumulated,therehavebeenmany attemptstodevisemoreefficientalgorithmsoffrequent itemsetmining.Manyofthemsharethesameideawith Aprioriinthattheygeneratecandidates.Theseinclude hash-basedtechnique,partitioning,samplingandusing verticaldataformat.Hash-basedtechniquecanreducethe sizeofcandidateitemsets.Eachitemsetishashedintoa 35Neutrosophic Sets and Systems, Vol. 8, 2015 M.M.Lotfy , Review of Recommender Systems Algorithms Utilized in Social Networks based e-Learning Systems & Neutrosophic Systemcorrespondingbucketbyusinganappropriatehash function.Sinceabucketcancontaindifferentitemsets,if its count is less than a minimum support, these itemsets in thebucketcanberemovedfromthecandidatesets.A partitioningcanbeusedtodividetheentiremining problemintonsmallerproblems.Thedatasetisdivided intonnon-overlappingpartitionssuchthateachpartition fitsintomainmemoryandeachpartitionismined separately.Sinceanyitemsetthatispotentiallyfrequent withrespecttotheentiredatasetmustoccurasafrequent itemsetinatleastoneofthepartitions,allthefrequent itemsetsfoundthiswayarecandidates,whichcanbe checkedbyaccessingtheentiredatasetonlyonce. Sampling is simply to mine a random sampled small subset oftheentiredata.Sincethereisnoguaranteethatwecan findallthefrequentitemsets,normalpracticeistousea lower support threshold. Trade off has to be made between accuracyandefficiency.Aprioriusesahorizontaldata format,i.e.frequentitemsetsareassociatedwitheach transaction. Using vertical data format is to use a different format in which transaction IDs (TIDs) are associated with eachitemset.Withthisformat,takingtheintersectionof TIDs can perform mining. The support count is simply the lengthoftheTIDsetfortheitemset.Thereisnoneedto scanthedatabasebecauseTIDsetcarriesthecomplete information required for computing support. The most outstanding improvement over Apriori would be amethodcalledFP-growth(frequentpatterngrowth)that succeededineliminatingcandidategeneration[14].It adoptsadivideandconquerstrategyby(1)compressing thedatabaserepresentingfrequentitemsintoastructure calledFP-tree(frequentpatterntree)thatretainsallthe essentialinformationand(2)dividingthecompressed databaseintoasetofconditionaldatabases,each associatedwithonefrequentitemsetandminingeachone separately.Itscansthedatabaseonlytwice.Inthefirst scan,allthefrequentitemsandtheirsupportcounts (frequencies) are derived and they are sorted in the order of descending support count in each transaction. In the second scan, items in each transaction are merged into a prefix tree anditems(nodes)thatappearincommonindifferent transactionsarecounted.Eachnodeisassociatedwithan item and its count. Nodes with the same label are linked by apointercallednode-link.Sinceitemsaresortedinthe descending order offrequency,nodesclosertotherootof theprefixtreearesharedbymoretransactions,thus resultinginaverycompactrepresentationthatstoresall the necessary information. Pattern growth algorithm works onFP-treebychoosinganitemintheorderofincreasing frequency and extracting frequent itemsets that contain the chosen item by recursively calling itself on the conditional FP-tree. FP-growth is an order of magnitude faster than the originalApriorialgorithm.Thereareseveralother dimensionsregardingtheextensionsoffrequentpattern mining. The major ones include the followings:(1)incorporatingtaxonomyinitems[15]:Useof Taxonomymakesitpossibletoextractfrequent itemsetsthatareexpressedbyhigherconcepts evenwhenuseofthebaselevelconcepts produces only infrequent itemsets.(2)incrementalmining:Inthissetting,itisassumed thatthedatabaseisnotstationaryandanew instanceoftransactionkeepsadded.The algorithmin[16]updatesthefrequentitemsets without restarting from scratch.(3)usingnumericvaluableforitem:Whentheitem correspondstoacontinuousnumericvalue, currentfrequentitemsetminingalgorithmisnot applicableunlessthevaluesarediscretized.A methodofsubspaceclusteringcanbeusedto obtainanoptimalvalueintervalforeachitemin each itemset [17].(4)usingothermeasuresthanfrequency,suchas informationgainorvalue:Thesemeasuresare usefulinfindingdiscriminativepatternsbut unfortunatelydonotsatisfyanti-monotonicity property.However,thesemeasureshaveanice propertyofbeingconvexwithrespecttotheir argumentsanditispossibletoestimatetheir upperboundforsupersetsofapatternandthus pruneunpromisingpatternsefficiently.Apriori SMP uses this principle [18].(5)usingricherexpressionsthanitemset:Many algorithmshavebeenproposedforsequences, treeandgraphstoenableminingfrommore complex data structure [19].Closeditemsets:Afrequentitemsetisclosedifitisnot includedinanyotherfrequentitemsets.Thus,oncethe closeditemsetsarefound,allthefrequentitemsetscanbe derived from them. LCM is the most efficient algorithm to find the closed itemsets [20]. 7 Neutrosophic System Thefirstinputparametertotheneutrosophicvariable the number Clusters has three membership function (Y) , non-membership(N)andindeterminacy(I)ofnis illustrated in Figure 1. 36Neutrosophic Sets and Systems, Vol. 8, 2015 M.M.Lotfy , Review of Recommender Systems Algorithms Utilized in Social Networks based e-Learning Systems & Neutrosophic SystemFigure1:Membershipfunction,non-membershipand indeterminacy of neutrosophic set with variable n. TheinputneutrosophicvariablethefrequencyofSubject Itemshasthemembershipfunctions,non-membershipand indeterminacy of fis showed in formulation (1) =no) non(yes,acyIndetermin variable is item subject the noconstant is item keythe yesfFormula 1 TheoutputneutrosophicvariableSubjectItemshas neutrosophicsets.Itshouldbenotedthatmodifyingthe membershipfunctions,non-membershipand indeterminacywillchangethesensitivityofthe neutrosophiclogicsystemsoutputtoitsinputs.Also increasing the number of neutrosophic sets of the variables willprovidebettersensitivitycontrolbutalsoincreases computationalcomplexityofthesystem.Table1and Figure 2show the rules used in the neutrosophic system. Table 1:The neutrosophic system rules Cluster membership functions non-membership indeterminacy 1YNI 2NIY 3IYN 4YNI Figure 2: show the graph neutrosophic system. The Neutrosophic System Equation Given by : B R A = Such ThatA : Represent Neutrosophic Data input for e-Learning System . R : RepresentProcessing Neutrosophic System Data . A :Represent Recommendation Subject for Students . TheoutputofthatsystemdeterminesthenumberofSub-ject Items Recommended. This determination is based on theNSanalysiswhishpassesthethreeparameters of ) ( ), ( ), ( x x x AA A Av o =where( ) ( ) x xA Ao ,and( ) xAv whichrepresentthedegreeofmembershipfunc-tion (namely ( ) xA ), the degree of indeterminacy (name-ly( ) xAo ),andthedegreeof non-membership(namely ( ) xAv )respectivelyofeachelementX xetothesetAwhere + s s 1 ) ( ), ( ), ( 0 x x xA A Av o and+ s + + s 3 ) ( ) ( ) ( 0 x x xA A Av o , then based on that anal-ysis the system decides the accurate key size in each situ-ation. 8 Conclusion and Future Work Inthispaper,wepresentedtheimportanceofsocial networksine-Learningsystems.Recommendersystems play important roles in e-Learning as they help students to choseamongdifferentlearningobjectstostudyand activities to participate in. Among the different objects and activitiesavailable,recommendersystemscanchose betweendifferentalgorithms.Presentedalgorithmsinthis paperare:C4.5,K-Means,SupportVectorMachine,and Apriorialgorithms.Eachofthosealgorithmsfitintoa certainfunctionalityoftherecommendersystem.Future workwillincludecomparisonbetweenotherimportant machine learning algorithms, and our proposed e-Learning modelthatutilizesdifferentmachinelearningalgorithms forsocialnetworksupportede-Learning.Wehave presentedaproposedeffectivee-Learningsystemthat utilizesanewlypresentedneutrosophicdatasetin analyzingsocialnetworkdataintegratedine-Learning. Identifying relationships between students is important for learning.Futureworkincludeincorporating theresults we haveachievedincustomizingcoursecontentstostudents, and recommending new learning objects more suitable for personalized learning. 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Math., 24 , 287 297.2005 [24]A.A.Salama,SaidBroumiandFlorentin Smarandache,NeutrosophicCrispOpenSetand NeutrosophicCrispContinuityviaNeutrosophic CrispIdeals,I.J.InformationEngineeringand Electronic Business, 2014, Vol.(3)pp1-8. [25]A.A.Salama,BasicStructureofSomeClasses ofNeutrosophicCrispNearlyOpenSetsand PossibleApplicationtoGISTopology, NeutrosophicSetsandSystems,2015,Vol.(7) pp18-22. [26] A.A.SalamaandSaidBroumi,Roughnessof NeutrosophicSets,ElixirAppl.Math.74 (2014)pp26833-26837. 38Neutrosophic Sets and Systems, Vol. 8, 2015 M.M.Lotfy , Review of Recommender Systems Algorithms Utilized in Social Networks based e-Learning Systems & Neutrosophic System[27]A.A.Salama,MohamedAbdelfattahand Mohamed Eisa, Distances, Hesitancy Degree and FlexibleQueryingviaNeutrosophicSets, InternationalJournalofComputerApplications, Volume 101 No.10, (2014)pp0975 8887 [28]I.M. Hanafy, A.A. Salama , M. Abdelfattahand Y.Wazery,SecurityinMantBasedonPkiusing FuzzyFunction"IOSRJournalofComputer Engineering, Vol.(6), ISSUE 3, (2012)pp 54-60. [29]M.M.Lofty,A.A.Salama,H.A.El-Ghareeb andM.A.El-dosuky,SubjectRecommendation UsingOntologyforComputerScienceACM Curricula,InternationalJournalofInformation ScienceandIntelligentSystem,Vol.3, (2014)pp199-205 [30]A.A.Salama,HaithemA.El-Ghareeb,Ayman. M.MaineandFlorentinSmarandache. IntroductiontoDevelopSomeSoftware ProgramsfordealingwithNeutrosophicSets, NeutrosophicSetsandSystems,2014,Vol(4), pp51-52. [31]A.A.Salama,F.Smarandache,andM.Eisa. IntroductiontoImageProcessingvia NeutrosophicTechnique,NeutrosophicSetsand Systems, 2014, Vol. (5) pp59-63. [32]A.A.Salama,MohamedAbdelfattahand MohamedEisa,ANovelModelfor ImplementingSecurityoverMobileAd-hoc NetworksusingIntuitionisticFuzzyFunction,InternationalJournalofEmergingTechnologies inComputationalandAppliedSciences (IJETCAS)2014, Vol.(7),no(1),pp01-07. [33]A.A.Salama,MohamedEisaandM.M.Abdelmoghny,NeutrosophicRelationsDatabase, InternationalJournalofInformationScienceand Intelligent System,3(1) (2014)pp33-46 . [34]A.A.Salama,HaithamA.El-Ghareeb,Ayman M.ManieandM.M.Lotfy,Utilizing NeutrosophicSetinSocialNetworkAnalysise-LearningSystems,InternationalJournalof Information Science and Intelligent System, 3(2), (2014)pp61-72. [35] A.A.Salama,MohamedAbdelfattahand MohamedEisa,ANovelModelfor ImplementingSecurityoverMobileAd-hoc NetworksusingIntuitionisticFuzzyFunction, InternationalJournalofEmergingTechnologies inComputationalandAppliedSciences (IJETCAS), 2014,Vol.(6)pp1-7. [36]I.M. Hanafy, A.A. Salama , M. Abdelfattah and Y.M.Wazery,AISMODELFORBOTNET DETECTIONINMANETUSINGFUZZY FUNCTION,InternationalJournalofComputer Networking,WirelessandMobile Communications(IJCNWMC),Vol.3,Issue1, Mar 2013, 95-102 . [37]A.A.Salama,MohamedAbdelfattahandS.A. Alblowi, Some Intuitionistic Topological Notions ofIntuitionisticRegion,PossibleApplicationto GISTopologicalRules,InternationalJournalof Enhanced Research in Management [38]I.M. Hanafy, A.A. Salama , M. Abdelfattahand Y.Wazery,SecurityinMantBasedonPkiusing FuzzyFunction,IOSRJournalofComputer Engineering, Vol.(6), ISSUE 3, (2012)pp 54-60. [39]A. A. Salama, Said Broumi, A Novel Model for ImplementingSecurityoverMobileAd-hoc NetworksviaNeutrosophicTechniques,2014 (Accepted). [40]A.A.Salama,SaidBroumiandFlorentin Smarandache,IntroductiontoNeutrosophic Topological Spatial Region, Possible Application to GIS Topological Rules (2014) (Accepted). [41]A.A.SalamaandS.A.Alblowi,Neutrosophic SetTheoryandNeutrosophicTopologicalIdeal Spaces,TheFirstInternationalConferenceon MathematicsandStatistics(ICMS10)to beheld at the American University. [42]A.A.SalamaandS.A.Alblowi,Neutrosophic Set and Neutrosophic Topological Space, ISOR J. mathematics(IOSR-JM),Vol.(3).Issue(4), (Sep-Oct. 2012). pp 31-35. [43]A.SalamaandS.A.Alblowi,Generalized Neutrosophic Set and Generalized Neutrousophic TopologicalSpaces,journal.sapub.org/computer Sci.JournalcomputerSci.Engineering,Vol.(2) No. (7) ((2012). [44] A.A.Salama,andH.Elagamy,Neutrosophic Filters,InternationalJournalofComputer 39Neutrosophic Sets and Systems, Vol. 8, 2015 M.M.Lotfy , Review of Recommender Systems Algorithms Utilized in Social Networks based e-Learning Systems & Neutrosophic SystemScience Engineering and Information Technology Reseearch(IJCSEITR),Vol.3,Issue1,Mar2013, pp307-312. [45]S.A.Alblowi,A.A.Salama&MohmedEisa, Newconceptsofneutrosophicsets,international journal of mathematics and computer applications research (ijmcar),vol. 4,(2014).[46] A.A.Salama,NeutrosophicCrispPoints& Neutrosophic Crisp Ideals, Neutrosophic Sets and Systems, Vol.1, No. 1,(2013) pp 50-54. [47]A.A.SalamaandF.Smarandache,Filtersvia NeutrosophicCrispSets,NeutrosophicSetsand Systems, Vol.1, No. 1,(2013) pp34-38.[48] A.A.SalamaandF.SmarandacheandS.A. Alblowi,TheCharacteristicFunctionofa NeutrosophicSet,NeutrosophicSetsand Systems, Vol.3, (2014)pp14-17. [49] A.A.SalamaandF.SmarandacheandValeriKroumov"NeutrosophicCrispSets& NeutrosophicCrispTopologicalSpaces " BulletinoftheResearchInstituteof Technology (OkayamaUniversityofScience, Japan),inJanuary-February2014.(Accepted)(Japan). [50]A.A.SalamaandFlorentinSmarandache, Filters via Neutrosophic Crisp Sets, Neutrosophic Sets and Systems, 2013, Vol. (1), pp34-37. [51]A.A.Salama,NeutrosophicCrispPoints& Neutrosophic Crisp Ideals, Neutrosophic Sets and Systems, 2013,Vol(1) pp50-53 [52]A.A.Salama,FlorentinSmarandache,Valeri Kroumov,NeutrosophicCrispSets& NeutrosophicCrispTopologicalSpaces, NeutrosophicSetsandSystems,2014,Vol.(2) pp25-30. [53]A.A.Salama,FlorentinSmarandacheand ValeriKroumov.NeutrosophicClosedSetand NeutrosophicContinuousFunctions, NeutrosophicSetsandSystems,2014,Vol.(4) pp4-8. [54]A.A.Salama,O.M.Khaled,andK.M. Mahfouz.NeutrosophicCorrelationandSimple LinearRegres-sion,NeutrosophicSetsand Systems, 2014, Vol. (5) pp3-8. [55]A.A.Salama,andF.Smarandache. NeutrosophicCrispSetTheory,Neutrosophic Sets and Systems, 2014, Vol. (5) pp27-35. [56]A. A. Salama , Florentin Smarandache and S. A. ALblowi,NewNeutrosophicCrispTopologicalConcepts,NeutrosophicSetsandSystems,2014, Vol(4)pp50-54. [57]A.A.SalamaandF.Smarandache,Filtersvia NeutrosophicCrispSets,NeutrosophicSetsand Systems, Vol.1, No. 1, pp. 34-38. 2013 [58]A.A.SalamaandS.A.Alblowi,Intuitionistic FuzzyIdealsTopologicalSpaces,Advancesin FuzzyMathematics,Vol.(7),Number1, (2012)pp51- 60.[59]A.A.Salama,andH.Elagamy,Neutrosophic Filters, International Journal of Computer Science EngineeringandInformationTechnology Reseearch (IJCSEITR), Vol.3, Issue(1), (2013)pp 307-312.[60]I.M.Hanafy,A.A.SalamaandK.M.Mahfouz, NeutrosophicCrispEventsandItsProbability, InternationalJournalofMathematicsand ComputerApplicationsResearch(IJMCAR) Vol.(3),Issue 1,Mar (2013), pp.171-178.[61] A.A.SalamaandS.A.Alblowi,Generalized NeutrosophicSetandGeneralizedNeutrosophic Spaces,JournalComputerSci.Engineering,Vol. (2) No. (7) (2012)pp129-132 .[62] A. A. Salama and S. A. Alblowi, Neutrosophic Set and Neutrosophic Topological Spaces, ISOR J.Mathematics,Vol.(3),Issue(3),(2012)pp-31-35.[63]S.A.Alblowi,A.A.SalamaandMohmedEisa, NewConceptsofNeutrosophicSets, InternationalJournalofMathematicsand Computer Applications Research (IJMCAR),Vol. 4, Issue 1,(2014) 59-66. [64]I.M.Hanafy,A.A.SalamaandK.Mahfouz, CorrelationofNeutrosophicData,International RefereedJournalofEngineeringandScience (IRJES), Vol.(1), Issue 2 . PP.39-33. 2012. 40Neutrosophic Sets and Systems, Vol. 8, 2015 M.M.Lotfy , Review of Recommender Systems Algorithms Utilized in Social Networks based e-Learning Systems & Neutrosophic System[65]A.A.Salama,F.SmarandacheandValeriKroumov,NeutrosophicCrispSets& Neutrosophic Crisp Topological Spaces,Bulletin oftheResearchInstituteof Technology (OkayamaUniversityofScience, Japan), in January-February 2014.(Japan).[66]A.A.Salama,TheConceptofNeutrosophicSet andBasicPropertiesofNeutrosophicSet Operations,WASET2012PARIS,FRANC, InternationalUniversityofScience,Engineering and Technology. Received: January 6, 2015. Accepted: March 4, 201541