PowKMeans: A Hybrid Approach for Gray Sheep Users Detection and Their Recommendations

Int. J. Inf. Technol. Web Eng. Pub Date : 2018-04-01 DOI:10.4018/IJITWE.2018040106

Honey Jindal, Shalini Agarwal, Neetu Sardana

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引用次数: 5

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

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PowKMeans:一种灰羊用户检测的混合方法及其建议

这篇文章描述了推荐系统是如何使用信息过滤技术生成个性化偏好的软件应用程序或web门户，其目标是支持decision-makingoftheusers。Collaborative-basedtechniquesareoftenusedtopredicttheunknown preferencesoftheuserbaseduponhispastpreferencesorthepreferencesofthesimilarusersthat havealreadybeenidentified。Auserwhichhasahighcorrelationwithanygroupofusersisknown aswhiteuserwhereastheuserswhichhavelesscorrelationwithanygroupofusersareknownas gray-sheepusers。Thepresenceofgray-sheepusersaffectstheaccuracyofthemodel，andgenerates inaccuratepredictions。Toimprovethepredictionaccuracy，itisimportanttodifferentiategraysheep usersfromwhiteusers。ExperimentalresultsshowthatPowKMeansiseffectiveinimprovingthe predictionaccuracyby4.62%.IthasalsoshownreductioninMeanAbsoluteErrorby0.7757。关键词协同过滤，灰羊用户，k - means++， PowKMeans，推荐系统

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Int. J. Inf. Technol. Web Eng.

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