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Toplam kayıt 100, listelenen: 61-80
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P2P collaborative filtering with privacy
(Tubitak Scientific & Technical Research Council Turkey, 2010)With the evolution of the Internet and e-commerce, collaborative filtering (CF) and privacy-preserving collaborative filtering (PPCF) have become popular The goal in CF is to generate predictions with decent accuracy, ... -
PEL: A Predictive Edge Linking algorithm
(Academic Press Inc Elsevier Science, 2016)We propose an edge linking algorithm that takes as input a binary edge map generated by a traditional edge detection algorithm and converts it to a set of edge segments; filling in one pixel gaps in the edge map, cleaning ... -
Phase portrait modeling of a nonlinear system with a dynamic fuzzy network
(Springer, 2005)Fuzzy logic and neural networks are two important technologies for modeling and control of dynamical systems and have been constrained by the non-dynamical nature of their some popular architectures. There exist problems ... -
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 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 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 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 ... -
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 ... -
Reconstructing rated items from perturbed data
(Elsevier Science BV, 2016)The basic idea behind privacy-preserving collaborative filtering schemes is to prevent data collectors from deriving the actual rating values and the rated items. Different data perturbation methods have been proposed to ... -
Robustness analysis of arbitrarily distributed data-based recommendation methods
(Pergamon-Elsevier Science LTD, 2016)Due to different shopping routines of people, rating preferences of many customers might be partitioned between two parties. Since two different e-companies might sell products from the same range to the identical set of ... -
Robustness analysis of multi-criteria collaborative filtering algorithms against shilling attacks
(Pergamon-Elsevier Science LTD, 2019)Collaborative filtering is an emerging recommender system technique that aims guiding users based on other customers preferences with behavioral similarities. Such correspondences are located based on preference history ...