Başlık için Bilgisayar Mühendisliği Bölümü listeleme
Toplam kayıt 216, listelenen: 149-168
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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 item-based recommendations over partitioned data with overlaps
(Inderscience Enterprises Ltd., 2017)User ratings are vital elements to drive recommender systems and, in the case of an insufficient amount of ratings, companies may prefer to operate recommender services over partitioned data. To make this feasible, there ... -
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 distributed data
(Springer Verlag, 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 normalized ratings-based weighted slope one predictor
(WITPress, 2016)Weighted Slope One predictor is proposed as a model-based collaborative filtering algorithm based on user ratings. The predictor is able to efficiently provide accurate predictions. The scheme utilizes user's true ratings. ... -
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 naïve Bayesian classifier-based private recommendations on partitioned data
(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 ...