Hybrid recommendation based on graph embedding

Zhou, Cheng; Zhang, Haifeng; Ren, Junwei; Wen, Chaodong; He, Peng

doi:10.23919/jcc.2021.11.017

Cited by 5 publications

(2 citation statements)

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“…A Chinese utterance is a unique sequence of characters consisting of a combination of words and phrases, and the same character can have different meanings in different sentences. In order for the computer to accurately comprehend the text and extract the main keywords, it must first perform a word separation operation to divide the text according to certain rules; hence, the development of jieba word separation [25]. Jieba is a popular third-party Python library for Chinese word splitting.…”

Section: Jieba Word Splitting Techniquesmentioning

confidence: 99%

Enhancement Economic System Based-Graph Neural Network in Stock Classification

Zhang

2023

IEEE Access

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As a result of the integration of the stock industry into the entire international economic system, stock companies publish hundreds of prospectuses every second. The ability to quickly and accurately classify the companies and categories to which these data belong, as well as improve the performance of the economic system, has become the key to unlocking its corresponding value at this stage. In order to solve this problem, graph neural network techniques are used to accomplish the classification of stocks in the science and technology version, thus indirectly alleviating the enormous pressure on the economic system. In this study, to complete semi-supervised classification, we propose the PA-GCN model, which takes the lead in graph attention calculation of stock nodes and introduces the Elu activation function. Specifically, in this study, we constructed a stock dataset and implemented a dropout layer to prevent overfitting. The final results on the stock dataset and the publicly accessible Cora dataset demonstrate that this strategy may effectively improve the economic system and that the model has good performance. In the two dataset tests, classification accuracy can be attained at 81.69% and 81.2%, respectively. It also demonstrates the method's viability for classifying stock nodes.

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Section: Jieba Word Splitting Techniquesmentioning

confidence: 99%

Enhancement Economic System Based-Graph Neural Network in Stock Classification

Zhang

2023

IEEE Access

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“…After using regular expressions to remove special symbols, the capitalization of letters is standardized to prevent other issues from arising in subsequent operations. Before utilizing jieba [33], [34] to segment the primary business of each stock in the cleansed data, it is necessary to eliminate certain words that have no bearing on stock classification. Reduce the number of irrelevant nodes in the future graph of stock text, hence lowering its complexity and accelerating the training.…”

Section: ) Cleaning Of Stock Characteristicsmentioning

confidence: 99%

Promoting Financial Market Development-Financial Stock Classification Using Graph Convolutional Neural Networks

Xu,

Zhang,

2023

IEEE Access

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According to surveys, one in seven people in China are involved in stock trading, and the role of stocks in the global economy is growing. In terms of the entire stock market, the Chinese market alone has over 4000 stocks. Faced with a chaotic and diverse assortment of stocks, it is necessary to categorize them. On the one hand, it can facilitate stock market research, and on the other, it can make it easier for stockholders to purchase shares. The graph convolutional neural network-based SK-GCN model developed in this paper delivers excellent results in the categorization of stock classes. This model employs two layers of convolutional layers and activation functions to effectively categorize stocks by incorporating external nodes to expand stock features and drawing inspiration from short text classification. This strategy is highly innovative and produces promising outcomes. In this paper, we constructed the dataset by crawling the information of all stocks listed on the GEM of Oriental Fortune website. We achieved an accuracy of 83.04% and a macro-F1 value of 0.8303 under the assumption of small sample training, and its classification effect is significantly superior to other classification models.

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Privacy-preserving recommendation system based on social relationships

Yu,

Wang,

et al. 2024

Journal of King Saud University - Computer and Information Scie

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Hybrid recommendation based on graph embedding

Cited by 5 publications

References 0 publications

Enhancement Economic System Based-Graph Neural Network in Stock Classification

Enhancement Economic System Based-Graph Neural Network in Stock Classification

Promoting Financial Market Development-Financial Stock Classification Using Graph Convolutional Neural Networks

Privacy-preserving recommendation system based on social relationships

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