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The Cubic Factor (CF) method and Weibull model have been subjected in the present analysis.
Out of these, the third approach is highlighted as optimum CF method (Kahraman 2012; Mungwattana 2000).
We use online reviews as an auxiliary information source and design a hybrid CF method to address the sparsity problem.
Meanwhile, clustering algorithms are also combined with the CF method to group together similar movies before recommendation [26].
KNN(_u): This is user-based CF method, where user-user similarity is calculated based on the training data.
For single burning particles, the CF method resulted in k= 0.51 ±0.05)×102 s/cm2, which was confirmed by direct measurement of the combustion time.
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CF methods build models relied only on past user behaviors, i.e., CF methods focus only on the user-item activities which can be users' previous transactions or product ratings.
CF methods make recommendations based on a group of users outside the sample group with similar film preferences.
The differences were sought with regard to the necessity and frequency of CF; methods of CF, timing, types of errors, and choice of correctors.
The aim of this study is to address these privacy issues in the context of neighborhood-based CF methods by proposing a Private Neighbor Collaborative Filtering (PNCF) algorithm.
Then, MF [2, 3, 4] methods play an important role in model-based CF methods, which aim to learn latent factors on user-item matrix.
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