․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․Collaborative Filtering (CF) predicts the focal user's preference for particular item based on user's preference rating data and recommends items for the similar users by using them. It is a popular technique for the personalization in e-commerce to reduce information overload. However, it has some limitations including sparsity and scalability problems. In this paper, we use a method to integrate social network information into collaborative filtering in order to mitigate the sparsity and scalability problems which are major limitations of typical collaborative filtering and reflect the user's qualitative and emotional information in recommendation process. In this paper, we use a novel recommendation algorithm which is integrated with collaborative filtering by using Social SVD++ algorithm which considers social network information in SVD++, an extension algorithm that can reflect implicit information in singular value decomposition (SVD). In particular, this study will evaluate the performance of the model by reflecting the real-world user's social network information in the recommendation process.
A new method to determine biasdependent source resistances for GaAs MESFETs is introduced. The method is a combination of the cold-and pinched-FET measurement techniques based on the real parts of the two-port impedances and their derivatives. The proposed method offers a unique extraction procedure only assuming that the channel doping profile is symmetric. The deleterious problems of negative source resistances in the pinched-FET condition and the deviation of a 2 1 and a12 from 0.5 in the cold-FET condition are solved by deembedding the on-wafer pad parasitics. The usefulness of the method has been demonstrated by extracting source resistances for two types of MESFETs. The results were self-consistent enough to confirm the validity of this method.
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