Recommender systems based on opinion mining and deep neural networks

To address rating sparsity problem, various review-based recommender systems have been developed in recent years. Most of them extract topics, opinions, and emotional polarity from the reviews by using the techniques of text analysis and opinion mining. According to existing researches, review-based recommendation methods utilize review elements in rating prediction model, but underuse the actual ratings provided by users. In this paper, we adopt one lexicon-based opinion mining method to extract opinions hidden in reviews, and also, we combine opinions with actual ratings. In addition, we embed deep neural networks model which breaks through the limitation of traditional collaborative filtering. The experimental results based on two public datasets indicate that this personalized model provides an effective recommendation performance.

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Alzheimer Disease Prediction Model Based on Decision Fusion of CNN-BiLSTM Deep Neural Networks

Advances in Intelligent Systems and Computing - Intelligent Systems and Applications ◽

10.1007/978-3-030-55190-2_36 ◽

2020 ◽

pp. 482-492

Author(s):

Shaker El-Sappagh ◽

Tamer Abuhmed ◽

Kyung Sup Kwak

Keyword(s):

Neural Networks ◽

Alzheimer Disease ◽

Prediction Model ◽

Deep Neural Networks ◽

Decision Fusion ◽

Disease Prediction ◽

Model Based

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Forecast Model of TV Show Rating Based on Convolutional Neural Network

Complexity ◽

10.1155/2021/6694538 ◽

2021 ◽

Vol 2021 ◽

pp. 1-10

Author(s):

Lingfeng Wang

Keyword(s):

Neural Network ◽

Neural Networks ◽

Prediction Model ◽

Convolutional Neural Network ◽

Network Structure ◽

Rating Data ◽

Tv Program ◽

Neural Network Structure ◽

Rating Prediction ◽

Tv Show

The TV show rating analysis and prediction system can collect and transmit information more quickly and quickly upload the information to the database. The convolutional neural network is a multilayer neural network structure that simulates the operating mechanism of biological vision systems. It is a neural network composed of multiple convolutional layers and downsampling layers sequentially connected. It can obtain useful feature descriptions from original data and is an effective method to extract features from data. At present, convolutional neural networks have become a research hotspot in speech recognition, image recognition and classification, natural language processing, and other fields and have been widely and successfully applied in these fields. Therefore, this paper introduces the convolutional neural network structure to predict the TV program rating data. First, it briefly introduces artificial neural networks and deep learning methods and focuses on the algorithm principles of convolutional neural networks and support vector machines. Then, we improve the convolutional neural network to fit the TV program rating data and finally apply the two prediction models to the TV program rating data prediction. We improve the convolutional neural network TV program rating prediction model and combine the advantages of the convolutional neural network to extract effective features and good classification and prediction capabilities to improve the prediction accuracy. Through simulation comparison, we verify the feasibility and effectiveness of the TV program rating prediction model given in this article.

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Eigen Solution of Neural Networks and Its Application in Prediction and Analysis of Controller Parameters of Grinding Robot in Complex Environments

Complexity ◽

10.1155/2019/5296123 ◽

2019 ◽

Vol 2019 ◽

pp. 1-21 ◽

Cited By ~ 1

Author(s):

Shixi Tang ◽

Jinan Gu ◽

Keming Tang ◽

Wei Ding ◽

Zhengyang Shang

Keyword(s):

Neural Network ◽

Neural Networks ◽

Prediction Model ◽

Deep Neural Networks ◽

Complete Solution ◽

Partial Solution ◽

Complex Environments ◽

Solution Theory ◽

Analysis Model ◽

Neural Network Structure

The robot dynamic model is often rarely known due to various uncertainties such as parametric uncertainties or modeling errors existing in complex environments. It is a key problem to find the relationship between the changes of neural network structure and the changes of input and output environments and their mutual influences. Firstly, this paper defined the conceptions of neural network solution, neural network eigen solution, neural network complete solution, and neural network partial solution and the conceptions of input environments, output environments, and macrostructure of neural networks. Secondly, an eigen solution theory of general neural networks was proposed and proven including consistent approximation theorem, eigen solution existence theorem, consistency theorem of complete solution, the partial solution, and none solution theorem of neural networks. Lastly, to verify the eigen solution theory of neural networks, the proposed theory was applied to a novel prediction and analysis model of controller parameters of grinding robot in complex environments with deep neural networks and then build prediction model with deep learning neural networks for controller parameters of grinding robot. The morphological subfeature graph with multimoment was constructed to describe the block surface morphology using rugosity, standard deviation, skewness, and kurtosis. The results of theoretical analysis and experimental test show that the output traits have an optional effect with joint action. When the input features functioning in prediction increase, higher predicted accuracy can be obtained. And when the output traits involving in prediction increase, more output traits can be predicted. The proposed prediction and analysis model with deep neural networks can be used to find and predict the inherent laws of the data. Compared with the traditional prediction model, the proposed model can predict output features simultaneously and is more stable.

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A New Click-Through Rates Prediction Model Based on Deep&Cross Network

Algorithms ◽

10.3390/a13120342 ◽

2020 ◽

Vol 13 (12) ◽

pp. 342

Author(s):

Guojing Huang ◽

Qingliang Chen ◽

Congjian Deng

Keyword(s):

Neural Networks ◽

Prediction Model ◽

Deep Neural Networks ◽

Prediction Models ◽

State Of The Art ◽

Online Advertising ◽

Optimization Technique ◽

Proposed Model ◽

Great Progress ◽

Online Advertisement

With the development of E-commerce, online advertising began to thrive and has gradually developed into a new mode of business, of which Click-Through Rates (CTR) prediction is the essential driving technology. Given a user, commodities and scenarios, the CTR model can predict the user’s click probability of an online advertisement. Recently, great progress has been made with the introduction of Deep Neural Networks (DNN) into CTR. In order to further advance the DNN-based CTR prediction models, this paper introduces a new model of FO-FTRL-DCN, based on the prestigious model of Deep&Cross Network (DCN) augmented with the latest optimization technique of Follow The Regularized Leader (FTRL) for DNN. The extensive comparative experiments on the iPinYou datasets show that the proposed model has outperformed other state-of-the-art baselines, with better generalization across different datasets in the benchmark.

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Group-Wise Dynamic Dropout Based on Latent Semantic Variations

Proceedings of the AAAI Conference on Artificial Intelligence ◽

10.1609/aaai.v34i07.6782 ◽

2020 ◽

Vol 34 (07) ◽

pp. 11229-11236

Author(s):

Zhiwei Ke ◽

Zhiwei Wen ◽

Weicheng Xie ◽

Yi Wang ◽

Linlin Shen

Keyword(s):

Neural Networks ◽

Deep Neural Networks ◽

Semantic Information ◽

State Of The Art ◽

Classification Performance ◽

Network Robustness ◽

Feature Detectors ◽

Data Points ◽

Adversarial Examples ◽

Public Datasets

Dropout regularization has been widely used in various deep neural networks to combat overfitting. It works by training a network to be more robust on information-degraded data points for better generalization. Conventional dropout and variants are often applied to individual hidden units in a layer to break up co-adaptations of feature detectors. In this paper, we propose an adaptive dropout to reduce the co-adaptations in a group-wise manner by coarse semantic information to improve feature discriminability. In particular, we showed that adjusting the dropout probability based on local feature densities can not only improve the classification performance significantly but also enhance the network robustness against adversarial examples in some cases. The proposed approach was evaluated in comparison with the baseline and several state-of-the-art adaptive dropouts over four public datasets of Fashion-MNIST, CIFAR-10, CIFAR-100 and SVHN.

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CrowdTC: Crowd-powered Learning for Text Classification

ACM Transactions on Knowledge Discovery from Data ◽

10.1145/3457216 ◽

2021 ◽

Vol 16 (1) ◽

pp. 1-23

Author(s):

Keyu Yang ◽

Yunjun Gao ◽

Lei Liang ◽

Song Bian ◽

Lu Chen ◽

...

Keyword(s):

Neural Network ◽

Neural Networks ◽

Text Classification ◽

Deep Neural Networks ◽

Semantic Information ◽

Human Beings ◽

Hybrid Neural Network ◽

Public Datasets ◽

Almost All ◽

The Cost

Text classification is a fundamental task in content analysis. Nowadays, deep learning has demonstrated promising performance in text classification compared with shallow models. However, almost all the existing models do not take advantage of the wisdom of human beings to help text classification. Human beings are more intelligent and capable than machine learning models in terms of understanding and capturing the implicit semantic information from text. In this article, we try to take guidance from human beings to classify text. We propose Crowd-powered learning for Text Classification (CrowdTC for short). We design and post the questions on a crowdsourcing platform to extract keywords in text. Sampling and clustering techniques are utilized to reduce the cost of crowdsourcing. Also, we present an attention-based neural network and a hybrid neural network to incorporate the extracted keywords as human guidance into deep neural networks. Extensive experiments on public datasets confirm that CrowdTC improves the text classification accuracy of neural networks by using the crowd-powered keyword guidance.

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Deep neural networks meet recommender systems

Big Data Recommender Systems - Volume 2: Application Paradigms ◽

10.1049/pbpc035g_ch2 ◽

2019 ◽

pp. 9-33

Author(s):

Shuai Zhang ◽

Lina Yao ◽

Aixin Sun ◽

Guibing Guo ◽

Xiwei Xu ◽

...

Keyword(s):

Neural Networks ◽

Recommender Systems ◽

Deep Neural Networks

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Benchmarking MRI Reconstruction Neural Networks on Large Public Datasets

Applied Sciences ◽

10.3390/app10051816 ◽

2020 ◽

Vol 10 (5) ◽

pp. 1816 ◽

Cited By ~ 3

Author(s):

Zaccharie Ramzi ◽

Philippe Ciuciu ◽

Jean-Luc Starck

Keyword(s):

Neural Networks ◽

Deep Neural Networks ◽

Resonance Imaging ◽

Mr Image ◽

Space Network ◽

Mri Reconstruction ◽

Public Dataset ◽

Magnetic Resonance Imaging Mri ◽

Public Datasets ◽

Space Data

Deep learning is starting to offer promising results for reconstruction in Magnetic Resonance Imaging (MRI). A lot of networks are being developed, but the comparisons remain hard because the frameworks used are not the same among studies, the networks are not properly re-trained, and the datasets used are not the same among comparisons. The recent release of a public dataset, fastMRI, consisting of raw k-space data, encouraged us to write a consistent benchmark of several deep neural networks for MR image reconstruction. This paper shows the results obtained for this benchmark, allowing to compare the networks, and links the open source implementation of all these networks in Keras. The main finding of this benchmark is that it is beneficial to perform more iterations between the image and the measurement spaces compared to having a deeper per-space network.

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