Seungyeon Kim

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Personalized recommendation systems are used in a wide variety of applications such as electronic commerce, social networks, web search, and more. Collaborative filtering approaches to recommendation systems typically assume that the rating matrix (e.g., movie ratings by viewers) is low-rank. In this paper, we examine an alternative approach in which the(More)
Matrix approximation is a common tool in recommendation systems, text mining, and computer vision. A prevalent assumption in constructing matrix approximations is that the partially observed matrix is low-rank. In this paper, we propose, analyze, and experiment with two procedures, one parallel and the other global, for constructing local matrix(More)
BACKGROUND The aims of this study were to observe the respiratory syncytial virus (RSV) hospitalization rate and to identify the risk factors for hospitalization for RSV infection among infants in Korea born at <35 weeks of gestational age and who had not previously received palivizumab. METHODS We conducted a study over a 2.5-year period (between April(More)
OBJECTIVE Previous neuroimaging studies on romantic love have focused on determining how the visual stimuli that serve as a representation of loved ones induce the neural activation patterns of romantic love. The purpose of this study was to investigate the temporal changes in romantic love over a period of 6 months and their correlated neurophysiological(More)
Sentiment analysis predicts the presence of positive or negative emotions in a text document. In this paper we consider higher dimensional extensions of the sentiment concept, which represent a richer set of human emotions. Our approach goes beyond previous work in that our model contains a continuous manifold rather than a finite set of human emotions. We(More)
Recent approaches to collaborative filtering have concentrated on estimating an algebraic or statistical model, and using the model for predicting missing ratings. In this paper we observe that different models have relative advantages in different regions of the input space. This motivates our approach of using stagewise linear combinations of(More)
Matrix approximation is a common tool in machine learning for building accurate prediction models for recommendation systems, text mining, and computer vision. A prevalent assumption in constructing matrix approximations is that the partially observed matrix is of low-rank. We propose a new matrix approximation model where we assume instead that the matrix(More)