Bölüm "Anadolu Üniversitesi, Mühendislik Fakültesi, Bilgisayar Mühendisliği Bölümü" WoS İndeksli Yayınlar Koleksiyonu için listeleme
Toplam kayıt 154, listelenen: 101-120
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Privacy-Preserving Concordance-based Recommendations on Vertically Distributed Data
(IEEE, 2012)Recommender systems are attractive components of e-commerce. Customers apply such systems to get help for choosing the appropriate product to purchase. To provide accurate and dependable referrals, recommender systems ... -
Privacy-preserving Eigentaste-based collaborative filtering
(Springer-Verlag Berlin, 2007)With the evolution of e-commerce, privacy is becoming a major concern. Many e-companies employ collaborative filtering (CF) techniques to increase their sales by providing truthful recommendations to customers. Many ... -
Privacy-preserving hybrid collaborative filtering on cross distributed data
(Springer London LTD, 2012)Data collected for collaborative filtering (CF) purposes might be cross distributed between two online vendors, even competing companies. Such corporations might want to integrate their data to provide more precise and ... -
Privacy-Preserving Inverse Distance Weighted Interpolation
(Springer Heidelberg, 2014)Inverse distance weighted (IDW) interpolation is one of the well-known geo-statistics techniques. On the one hand, one party (server) holding some measurements for specific locations wants to provide predictions; on the ... -
Privacy-Preserving Kriging Interpolation on Distributed Data
(Springer-Verlag Berlin, 2014)Kriging is one of the most preferred geostatistical methods in many engineering fields. Basically, it creates a model using statistical properties of all measured points in the region, where a prediction value is sought. ... -
Privacy-preserving kriging interpolation on partitioned data
(Elsevier Science BV, 2014)Kriging is well-known, frequently applied method in geo-statistics. Its success primarily depends on the total number of measurements for some sample points. If there are sufficient sample points with measurements, kriging ... -
Privacy-Preserving Naive Bayesian Classifier-Based Recommendations on Distributed Data
(Wiley, 2015)Data collected for recommendation purposes might be distributed among various e-commerce sites, which can collaboratively provide more accurate predictions. However, because of privacy concerns, they might not want to work ... -
Privacy-Preserving Random Projection-Based Recommendations Based on Distributed Data
(World Scientific Publ Co Pte LTD, 2013)Providing recommendations based on distributed data has received an increasing amount of attention because it offers several advantages. Online vendors who face problems caused by a limited amount of available data want ... -
Privacy-preserving SOM-based recommendations on horizontally distributed data
(Elsevier Science BV, 2012)To produce predictions with decent accuracy, collaborative filtering algorithms need sufficient data. Due to the nature of online shopping and increasing amount of online vendors, different customers' preferences about the ... -
Privacy-Preserving Svd-Based Collaborative Filtering on Partitioned Data
(World Scientific Publ Co Pte LTD, 2010)Collaborative filtering (CF) systems are widely employed by many e-commerce sites for providing recommendations to their customers. To recruit new customers, retain the current ones, and gain competitive edge over competing ... -
Privacy-preserving top-N recommendation on distributed data
(Wiley, 2008)Traditional collaborative filtering (CF) systems perform filtering tasks on existing databases; however, data collected for recommendation purposes may split between different online vendors. To generate better predictions, ... -
Privacy-Preserving Trust-based Recommendations on Vertically Distributed Data
(IEEE Computer Soc, 2011)Providing recommendations on trusts between entities is receiving increasing attention lately. Customers may prefer different online vendors for shopping. Thus, their preferences about various products might be distributed ... -
Privacy-Preserving Two-Party Collaborative Filtering on Overlapped Ratings
(Ksii-Kor Soc Internet Information, 2014)To promote recommendation services through prediction quality, some privacy-preserving collaborative filtering solutions are proposed to make e-commerce parties collaborate on partitioned data. It is almost probable that ... -
Private predictions on hidden Markov models
(Springer, 2010)Hidden Markov models (HMMs) are widely used in practice to make predictions. They are becoming increasingly popular models as part of prediction systems in finance, marketing, bio-informatics, speech recognition, signal ... -
Providing naive Bayesian classifier-based private recommendations on partitioned data
(Springer-Verlag Berlin, 2007)Data collected for collaborative filtering (CF) purposes might be split between various parties. Integrating such data is helpful for both e-companies and customers due to mutual advantageous. However, due to privacy ... -
Providing predictions on distributed HMMs with privacy
(Springer, 2007)As forecasting is increasingly becoming important, hidden Markov models (HMMs) are widely used for prediction in many applications such as finance, marketing, bioinformatics, speech recognition, and so on. After creating ... -
Providing Private Recommendations on Personal Social Networks
(Springer-Verlag Berlin, 2010)Personal social networks are recently used to offer recommendations. Due to privacy concerns, privacy protection while generating accurate referrals is imperative. Since accuracy and privacy are conflicting goals, providing ... -
Providing private recommendations using naive Bayesian classifier
(Springer-Verlag Berlin, 2007)Today's CF systems fail to protect users' privacy. Without privacy protection, it becomes a challenge to collect sufficient and high quality data for CF. With privacy protection, users feel comfortable to provide more ... -
QoS guarantee for multimedia traffic in smart homes
(Springer, 2010)With the advent of home networking and widespread deployment of broadband connectivity to homes, a wealth of new services with real-time Quality of Service (QoS) requirements have emerged, e.g., Video on Demand (VoD), IP ... -
Quantification of Projective Distortion for Fiducial Markers
(IEEE, 2013)The aim of this study is to quantify the projective distortion of candidate quadrilaterals found in a square-framed fiducial marker detection algorithm. Based on the quantified value, candidates can be eliminated in such ...