{"title":"PowKMeans:一种灰羊用户检测的混合方法及其建议","authors":"Honey Jindal, Shalini Agarwal, Neetu Sardana","doi":"10.4018/IJITWE.2018040106","DOIUrl":null,"url":null,"abstract":"This article describes how recommender systems are software applications or web portals that generate personalized preferences using information filtering techniques, with a goal to support decision-makingoftheusers.Collaborative-basedtechniquesareoftenusedtopredicttheunknown preferencesoftheuserbaseduponhispastpreferencesorthepreferencesofthesimilarusersthat havealreadybeenidentified.Auserwhichhasahighcorrelationwithanygroupofusersisknown aswhiteuserwhereastheuserswhichhavelesscorrelationwithanygroupofusersareknownas gray-sheepusers.Thepresenceofgray-sheepusersaffectstheaccuracyofthemodel,andgenerates inaccuratepredictions.Toimprovethepredictionaccuracy,itisimportanttodifferentiategraysheep usersfromwhiteusers.ExperimentalresultsshowthatPowKMeansiseffectiveinimprovingthe predictionaccuracyby4.62%.IthasalsoshownreductioninMeanAbsoluteErrorby0.7757. KEyWoRDS Collaborative Filtering, Gray-Sheep Users, K-Means++, PowKMeans, Recommender System","PeriodicalId":222340,"journal":{"name":"Int. J. Inf. Technol. Web Eng.","volume":"34 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2018-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"5","resultStr":"{\"title\":\"PowKMeans: A Hybrid Approach for Gray Sheep Users Detection and Their Recommendations\",\"authors\":\"Honey Jindal, Shalini Agarwal, Neetu Sardana\",\"doi\":\"10.4018/IJITWE.2018040106\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This article describes how recommender systems are software applications or web portals that generate personalized preferences using information filtering techniques, with a goal to support decision-makingoftheusers.Collaborative-basedtechniquesareoftenusedtopredicttheunknown preferencesoftheuserbaseduponhispastpreferencesorthepreferencesofthesimilarusersthat havealreadybeenidentified.Auserwhichhasahighcorrelationwithanygroupofusersisknown aswhiteuserwhereastheuserswhichhavelesscorrelationwithanygroupofusersareknownas gray-sheepusers.Thepresenceofgray-sheepusersaffectstheaccuracyofthemodel,andgenerates inaccuratepredictions.Toimprovethepredictionaccuracy,itisimportanttodifferentiategraysheep usersfromwhiteusers.ExperimentalresultsshowthatPowKMeansiseffectiveinimprovingthe predictionaccuracyby4.62%.IthasalsoshownreductioninMeanAbsoluteErrorby0.7757. KEyWoRDS Collaborative Filtering, Gray-Sheep Users, K-Means++, PowKMeans, Recommender System\",\"PeriodicalId\":222340,\"journal\":{\"name\":\"Int. J. Inf. Technol. Web Eng.\",\"volume\":\"34 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2018-04-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"5\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Int. J. Inf. Technol. Web Eng.\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.4018/IJITWE.2018040106\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Int. J. Inf. Technol. Web Eng.","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.4018/IJITWE.2018040106","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 5
PowKMeans: A Hybrid Approach for Gray Sheep Users Detection and Their Recommendations
This article describes how recommender systems are software applications or web portals that generate personalized preferences using information filtering techniques, with a goal to support decision-makingoftheusers.Collaborative-basedtechniquesareoftenusedtopredicttheunknown preferencesoftheuserbaseduponhispastpreferencesorthepreferencesofthesimilarusersthat havealreadybeenidentified.Auserwhichhasahighcorrelationwithanygroupofusersisknown aswhiteuserwhereastheuserswhichhavelesscorrelationwithanygroupofusersareknownas gray-sheepusers.Thepresenceofgray-sheepusersaffectstheaccuracyofthemodel,andgenerates inaccuratepredictions.Toimprovethepredictionaccuracy,itisimportanttodifferentiategraysheep usersfromwhiteusers.ExperimentalresultsshowthatPowKMeansiseffectiveinimprovingthe predictionaccuracyby4.62%.IthasalsoshownreductioninMeanAbsoluteErrorby0.7757. KEyWoRDS Collaborative Filtering, Gray-Sheep Users, K-Means++, PowKMeans, Recommender System
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