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Toplam kayıt 7, listelenen: 1-7
Effects of similarity measures on the quality of predictions
(2013)
Providing accurate predictions efficiently is vital for the success of recommender systems. There are various factors that might affect the quality of the predictions and online performance. Similarity metric used to ...
SWCD: a sliding window and self-regulated learning-based background updating method for change detection in videos
(Is&T & Spie, 2018)
Change detection with background subtraction process remains to be an unresolved issue and attracts research interest due to challenges encountered on static and dynamic scenes. The key challenge is about how to update ...
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 ...
An intelligent control of chaos in lorenz system, with a dynamic wavelet network
(2004)
This paper proposes a dynamic wavelet network based intelligent adaptive controller design to regulate the chaotic states of the Lorenz equations. The "Dynamic Wavelet Network (OWN)" has lag dynamics, non-orthogonal mother ...
Designing shilling attacks on disguised binary data
(Inderscience Enterprises LTD, 2017)
Privacy-preserving collaborative filtering methods are effectual ways of coping with information overload problem while protecting confidential data. Their success depends on the quality of the collected data. However, ...
Robustness analysis of naïve Bayesian classifier-based collaborative filtering
(Springer Verlag, 2013)
In this study, binary forms of previously defined basic shilling attack models are proposed and the robustness of naïve Bayesian classifierbased collaborative filtering algorithm is examined. Real data-based experiments ...
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 ...