精神分裂症患者情绪存在不一致性（emotion paradox）：在自我报告上与正常人存在差异，但是在实验室任务中，对非当前的正性情绪刺激表现出更低的水平，该如何解释呢？本研究从认知缺损的角度解释患者这一异常表现... || anhedonia schizophrenia emotion paradox; || Gregory P. Strauss; James M. Gold...
Most real-world recommender services measure their performance based on the top-N results shown to the end users. Thus, advances in top-N recommendation have far-ranging consequences in practical applications. In this paper, we present a novel method, called Collaborative Denoising Auto-Encoder (CDA... || Recommender Systems; Collaborative Filtering; Denoising Auto- Encoders; 有关于模型的分类总结; DAE; || Yao Wu Christopher DuBois Alice X. Zheng Martin Ester...
Sparseness of user-to-item rating data is one of the major factors that deteriorate the quality of recommender system. To handle the sparsity problem, several recommendation techniques have been proposed that additionally consider auxiliary information to improve rating prediction accuracy. In parti... || Collaborative Filtering; Document Modeling; Contexual Information; Deep learning; CNN; || Donghyun Kim 1 , Chanyoung Park 1 , Jinoh Oh 1 , Sungyoung Lee 2 , Hwanjo Yu ∗1...
Many existing approaches to collaborative filtering can neither handle very large datasets nor easily deal with users who have very few ratings. In this paper we present the Probabilistic Matrix Factorization (PMF) model which scales linearly with the number of observations and, more importantly, pe... || PMF; probabilisitc graphical model; 矩阵系数问题; sparse; sparsity; 1659 citation; || Ruslan Salakhutdinov and Andriy Mnih...
Collaborative filtering(CF) is a widely used approach in recommender systems to solve many real-world problems. Traditional CF-based methods employ the user-item matrix which encodes the individual preferences of users for items for learning to make recommendation. In real applications, the rating m... || Deep learning ; DAE; AutoEncoder; MF: CF; || Xin Dong, Lei Yu, Zhonghuo Wu, Yuxia Sun, Lingfeng Yuan, Fangxi Zhang...
Most of the existing approaches to collaborative filtering cannot handle very large data sets. In this paper we show how a class of two-layer undirected graphical mod- els, called Restricted Boltzmann Machines (RBM’s), can be used to model tabular data, such as user’s ratings of movies. We present e... ; || Ruslan Salakhutdinov, Andriy Mnih, Geoffrey Hinton...
Sparse Linear Methods (SLIM) are state-of-the-art recommendation approaches based on matrix factorization, which rely on a regularized l 1 -norm and l 2 -norm optimization –an alternative optimization problem to the traditional Frobenious norm. Although they have shown outstanding performance in Top... || SLIM; GSLIM; latent factor vectors; encoding; prototype matrix; Orthogonal Matching Pursuit (OMP) algorithm; 原子; 信号分解; || Santiago Larraín, Denis Parra, Alvaro Soto...
The importance of accurate recommender systems has been widely recognized by academia and industry. How- ever, the recommendation quality is still rather low. Recently, a linear sparse and low-rank representation of the user-item matrix has been applied to produce Top-N recommendations. This approac... || 非凸秩估计; slim; || Zhao Kang Qiang Cheng...
