A rating prediction model with cross projection and evolving GCN for bitcoin trading network

2021 ◽  
Author(s):  
Kaicheng Zhang ◽  
Li Pan ◽  
Shijun Liu
Keyword(s):  
Prediction Model ◽  
Rating Prediction ◽  
Trading Network

scholarly journals Forecast Model of TV Show Rating Based on Convolutional Neural Network

Complexity ◽  
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.

scholarly journals Visual Classification of Music Style Transfer Based on PSO-BP Rating Prediction Model

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Tianjiao Li
Keyword(s):  
Prediction Model ◽  
Real Time ◽  
Nonlinear Problems ◽  
Style Transfer ◽  
Collaborative Filtering Algorithm ◽  
Item Attribute ◽  
Pixel Color ◽  
Rating Prediction ◽  
Time Changes

In this paper, based on computer reading and processing of music frequency, amplitude, timbre, image pixel, color filling, and so forth, a method of image style transfer guided by music feature data is implemented in real-time playback, using existing music files and image files, processing and trying to reconstruct the fluent relationship between the two in terms of auditory and visual, generating dynamic, musical sound visualization with real-time changes in the visualization. Although recommendation systems have been well developed in real applications, the limitations of CF algorithms are slowly coming to light as the number of people increases day by day, such as the data sparsity problem caused by the scarcity of rated items, the cold start problem caused by new items and new users. The work is dynamic, with real-time changes in music and sound. Taking portraits as an experimental case, but allowing users to customize the input of both music and image files, this new visualization can provide users with a personalized service of mass customization and generate personalized portraits according to personal preferences. At the same time, we take advantage of the BP neural network’s ability to handle complex nonlinear problems and construct a rating prediction model between the user and item attribute features, referred to as the PSO-BP rating prediction model, by combining the features of global optimization of particle swarm optimization algorithm, and make further improvements based on the traditional collaborative filtering algorithm.

scholarly journals Recommender systems based on opinion mining and deep neural networks

2018 ◽  
Vol 173 ◽  
pp. 03016 ◽  
Author(s):  
Jia Li ◽  
YongJian Yang
Keyword(s):  
Neural Networks ◽  
Prediction Model ◽  
Recommender Systems ◽  
Text Analysis ◽  
Deep Neural Networks ◽  
Opinion Mining ◽  
Mining Method ◽  
Rating Prediction ◽  
Sparsity Problem ◽  
Public Datasets

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.

scholarly journals Review Rating Prediction Based on User Context and Product Context

2018 ◽  
Vol 8 (10) ◽  
pp. 1849 ◽  
Author(s):  
Bingkun Wang ◽  
Shufeng Xiong ◽  
Yongfeng Huang ◽  
Xing Li
Keyword(s):  
Prediction Model ◽  
Language Processing ◽  
Product Information ◽  
Prediction Method ◽  
Training Data ◽  
Context Information ◽  
Research Focus ◽  
User Context ◽  
Online User Reviews ◽  
Rating Prediction

With the explosion of online user reviews, review rating prediction has become a research focus in natural language processing. Existing review rating prediction methods only use a single model to capture the sentiments of review texts, ignoring users who express the sentiment and products that are evaluated, both of which have great influences on review rating prediction. In order to solve the issue, we propose a review rating prediction method based on user context and product context by incorporating user information and product information into review texts. Our method firstly models the user context information of reviews, and then models the product context information of reviews. Finally, a review rating prediction method that is based on user context and product context is proposed. Our method consists of three main parts. The first part is a global review rating prediction model, which is shared by all users and all products, and it can be learned from training datasets of all users and all products. The second part is a user-specific review rating prediction model, which represents the user’s personalized sentiment information, and can be learned from training data of an individual user. The third part is a product-specific review rating prediction model, which uses training datasets of an individual product to learn parameter of the model. Experimental results on four datasets show that our proposed methods can significantly outperform the state-of-the-art baselines in review rating prediction.

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