Mazda A. Marvasti, A. Poghosyan, A. Harutyunyan, Naira Grigoryan
{"title":"Ranking and Updating Beliefs Based on User Feedback: Industrial Use Cases","authors":"Mazda A. Marvasti, A. Poghosyan, A. Harutyunyan, Naira Grigoryan","doi":"10.1109/ICAC.2015.29","DOIUrl":null,"url":null,"abstract":"Incorporation of user feedback in enterprise management products can greatly enhance our understanding of modern technology challenges and amplify the ability for those products to home in to user environments. In this paper we present an entropy-based confidence determination approach to process user feedback data (direct or indirect) to automatically rank and update the beliefs of any recommender system. Several examples of application of this method are discussed in the context of VMware products. Moreover, an optimization algorithm is demonstrated for adaptive thresholding of monitoring flows based on user ratings of generated alerts effectiveness.","PeriodicalId":6643,"journal":{"name":"2015 IEEE International Conference on Autonomic Computing","volume":"111 1","pages":"227-230"},"PeriodicalIF":0.0000,"publicationDate":"2015-07-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"4","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2015 IEEE International Conference on Autonomic Computing","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICAC.2015.29","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 4
Abstract
Incorporation of user feedback in enterprise management products can greatly enhance our understanding of modern technology challenges and amplify the ability for those products to home in to user environments. In this paper we present an entropy-based confidence determination approach to process user feedback data (direct or indirect) to automatically rank and update the beliefs of any recommender system. Several examples of application of this method are discussed in the context of VMware products. Moreover, an optimization algorithm is demonstrated for adaptive thresholding of monitoring flows based on user ratings of generated alerts effectiveness.
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