基于均衡模型的矛盾冲突分析在优化商品推荐算法中的应用

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1、APPLICATIONOFEQUILIBRIUMMODELBASEDANAI，YSIS0FCONFLICTINTHEOPTIM匝ZATIONOFCOMMODITYRECON眦NDATIONALGOIUTHMABSTRACTWhenrecommendcommodities，becauseasinglekindofrecommendationalgorithmownsshortcomings，SOintroducingotheralgorithmstoworkwithittoovercomethesedefects．However,introducingadditionalalgor

2、ithmswillbringdefectsofnewalgorithms，andrecommendingcommoditieswithmultiplealgorithmswillbringconflictsthenleadtobadresults．Gametheoryisusuallyusedasbasisformultipleobjectstomakeoptimaldecisioninconflictstate．Nashequilibriumisthebalancepointofagameinconflictstate．Theobjectswillgettheoptimalut

3、ilityvalueinagamewhentheyareinNashequilibrium．Therefore，usingNashequilibriumrelatedtheorytooptimizethecommodityrecommendationalgorithmwhichWasconsistedofvariousalgorithmsCansolvetheproblemsoftherecommendationalgorithm．Inordertosolvetheproblems，useoptimizationofrecommendationalgorithmwhichWasg

4、eneratedbycombiningcollaborativefilteringwithclusteringasanexampleanddothefollowingwork．First，accordingtothebasicelementsofagame，buildrelatedgamemodelbyanalyzingtheprocedureofcommodityrecommendationwhichworksWi廿lvariousalgorithms．Second，IIIanalyzeconflictsbetweendifferentalgorithmstoidentifyt

5、heNashequilibriumintheestablishedgamemodelthenestablishanequilibriummodel．Third，accordingtotheanalysisresultsfromtheequilibriummodelandthework：flowofstochasticallyoptimizationalgorithm，abstractastochasticoptimizationalgorithm．Forth，usethestochasticoptimizationalgorithmfromtheequilibriummodelt

6、ooptimizethecommodityrecommendationalgorithm．Fifth，doexperimentsonlargedatasetsforverifyingtheactualeffectsoftheoptimizationalgorithm．Theresultsoftheexperimentsshowthat：First,therecommendationalgorithmwhichwasoptimizedCanovercomethedefectsofrecommendingbyonesinglealgorithm．Second，theoptimized

7、recommendationalgorithmcancoordinatemultiplealgorithmswhichconsistofittoworkefficiently．Third，theootimizedrecommendationalgOnthincanMethip_,herqualitycommodityl一-一recommendationresults．Therefore，usingequilibriummodelbasedconflictanalysistoopt