Deep Matrix Factorization Models for Recommender Systems
andF denotes the function that maps the model parameters to the predicted scores. Based on this function, we can achieve our goal of recommending a set of items for an individual user to maximize the user’s satisfaction. Now, the next question is how to define such a functionF . Latent Factor Model (LFM) simply applied the dot product of p i, q
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Deep Neural Networks for High Dimension, Low …
www.ijcai.orgDeep Neural Networks for High Dimension, Low Sample Size Data Bo Liu, Ying Wei, Yu Zhang, Qiang Yang Hong Kong University of Science and Technology, Hong Kong
Training Feedforward Neural Networks Using …
www.ijcai.orgTraining Feedforward Neural Networks Using Genetic Algorithms David J. Montana and Lawrence Davis BBN Systems and Technologies Corp. 10 Mouiton St.
Learning Feature Engineering for Classification
www.ijcai.orgGiven a set of features and class labels, the classiÞer ... c!T of arityr, an ordered list of features[f i,...,f i+r ! 1] and a usefulness score. ... paradigm for each combination and selects top-useful ones.k In the following section, we describe how LFE learns and
Spatio-Temporal Graph Convolutional Networks: A Deep ...
www.ijcai.org3.2 Graph CNNs for Extracting Spatial Features The trafÞc network generally organizes as a graph structure. It is natural and reasonable to formulate road networks as graphs mathematically. However, previous studies neglect spatial attributes of trafÞc networks: the connectivity and globality of the networks are overlooked, since they are split
Imaging Time-Series to Improve Classification and Imputation
www.ijcai.orgiis the time stamp and Nis a con-stant factor to regularize the span of the polar coordinate sys-tem. This polar coordinate based representation is a novel way to understand time series. As time increases, correspond-ing values warp among different angular points on the span-ning circles, like water rippling. The encoding map of equa-
Recurrent Neural Network for Text Classification with ...
www.ijcai.orgFigure 2: Three architectures for modelling text with multi-task learning. Motivated by the success of multi-task learning [Caruana, 1997], we propose three multi-task models to leverage super-vised data from many related tasks. Deep neural model is well suited for multi-task learning since the features learned from a task may be useful for ...
L2,1-Norm Regularized Discriminative Feature Selection …
www.ijcai.orgrithms, e.g., Fisher score [Duda et al., 2001] , robust regres-sion [Nie et al., 2010], sparse multi-output regression [Zhao et al., 2010] and trace ratio [Nie et al., 2008], usually select featuresaccordingto labels of the training data. Because dis-criminative informationis enclosed in labels, supervised fea-
DeepFM: A Factorization-Machine based Neural Network …
www.ijcai.orgDeepFM: A Factorization-Machine based Neural Network for CTR Prediction Huifeng Guo 1, Ruiming Tang2, Yunming Yey1, Zhenguo Li2, Xiuqiang He2 1Shenzhen Graduate School, Harbin Institute of Technology, China 2Noah's Ark Research Lab, Huawei, China 1huifengguo@yeah.net, yeyunming@hit.edu.cn,2ftangruiming, li.zhenguo, hexiuqiangg@huawei.com Abstract Learning …
Detecting Rumors from Microblogs with Recurrent Neural ...
www.ijcai.orgDetecting Rumors from Microblogs with Recurrent Neural Networks Jing Ma,1 Wei Gao,2 Prasenjit Mitra,2 Sejeong Kwon,3 Bernard J. Jansen,2 Kam-Fai Wong,1 Meeyoung Cha3 1The Chinese University of Hong Kong, Hong Kong SAR 2Qatar Computing Research Institute, Hamad Bin Khalifa University, Qatar 3Graduate School of Culture Technology, Korea Advanced …
Time-Aware Multi-Scale RNNs for Time Series Modeling
www.ijcai.orgof genres requires modeling the emotional changes in music, which are controlled by note duration. Therefore, different scales are also needed at different time steps as the notes have different durations at different times [Hu et al., 2019]. Recently, some methods have been proposed to select ap-propriate scales corresponding to each sample ...
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