Image Recommendation Algorithm Combined with Deep Neural Network Designed for Social Networks

Du, Shiqiang; Chen, Zhenghan; Wu, Haoyan; Yi, Tang; Li, YuanQing

doi:10.1155/2021/5196190

Cited by 12 publications

(5 citation statements)

References 39 publications

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“…Classifiers based on linear representations have been widely used in many fields. Building on these observations, Du et al suggested a new competitive and categorization via cooperative representation that uses properties of training data with L2 parametric regularization to create a competitive environment that allows the correct class to contribute more to the encoding [14]. Chi et al proposed a new CRC-based classifier utilizing class-mean weighted discriminative corepresentation [15].…”

Section: Related Workmentioning

confidence: 99%

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Book Classification and Recommendation for University Libraries using Grey Correlation and Bayesian Probability

Cui,

Gao

2024

SCPE

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In today’s information era, collaborative filtering algorithms are widely used and their distinct knowledge discovery techniques can effectively address numerous issues. However, conventional collaborative filtering algorithms encounter cold-start and data sparsity issues, which restrict their performance and accuracy. The study selected the multi-feature method to improve the traditional collaborative filtering algorithm, and introduced gray correlation calculation and Bayesian probability for user preference analysis. A learning resource recommendation model based on collaborative filtering was developed by comparing the target user’s characteristics with those of other users, calculating their similarity, selecting users with high similarity to the target user and forming a neighbor set. Using Bayesian probability and grey correlation to analyze user preferences in library systems can be well applied in book classification and recommendation problems in university libraries. The computing layer, which includes the collaborative filtering calculation stage and the group recommendation calculation stage, is the model’s main functional component. The smaller the value of mean absolute error, the higher the prediction accuracy of the model. The mean absolute error value of the multi-feature collaborative filtering algorithm was inferior to the traditional collaborative filtering algorithm, indicating that the classification accuracy of the former is higher than that of the latter. When the training set to test set ratio steadily became bigger, the mean absolute error value reached the lowest and smoothest point at 80%. In dataset A, the minimum mean absolute error values of multi-feature collaborative filtering and collaborative filtering were 0.765 and 0.809. Compared with traditional filtering algorithms, the mean absolute error value has decreased by 0.044. In dataset B, the mean absolute error values of multi-feature collaborative filtering and collaborative filtering were 0.796 and 0.836. Compared with traditional filtering algorithms, the mean absolute error value has decreased by 0.040. In dataset C, the minimum mean absolute error values of multi-feature collaborative filtering and collaborative filtering were 0.815 and 0.848. Compared with traditional filtering algorithms, the mean absolute error value has decreased by 0.033. When the accuracy was the highest; the mean absolute error value was the smallest at the grey correlation, which means that the technique improves the reliability of the recommendations compared with other methods. This means that the method has a positive impact on the accuracy of the recommendations compared to other methods. Grey correlation degree can comprehensively consider the interrelationships between multiple factors, handle uncertain and incomplete information, and explore potential user needs and behavior patterns. The implementation of the grey correlation degree has transformed the collaborative filtering algorithm into a group filtering algorithm, thereby enhancing its precision. The research on book classification and recommendation in university libraries, which enhances the group filtering algorithm, can address a range of issues such as improving classification accuracy, augmenting recommendation diversity, enhancing library management efficiency among others. This, in turn, enables more precise book recommendations to users.

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Section: Related Workmentioning

confidence: 99%

“…In equation 4.4, P o k (c m |M ) is the likelihood that an element will appear in the occurrence M and P o k (M ) is the likelihood that a consumer will like a product. The likelihood that a user would adore a product when the user features is shown in equation (14).…”

Section: A Cf-based Book Recommendation Model For University Librariesmentioning

confidence: 99%

Book Classification and Recommendation for University Libraries using Grey Correlation and Bayesian Probability

Cui,

Gao

2024

SCPE

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“…The algorithm sorted user interaction records over time, combined feature algorithms to construct feature vectors, and constructed a long and short term memory (LSTM) neural network. Results showed that LSTM's performance was superior to traditional social network image recommendation algorithms [4]. To enhance the recommendation accuracy, scholars like Fang J proposed a Collaborative Filtering recommendation algorithm that leverages deep neural network fusion.…”

Section: Related Workmentioning

confidence: 99%

A Graph Neural Network Recommendation Method Integrating Multi Head Attention Mechanism and Improved Gated Recurrent Unit Algorithm

Liu,

Wang,

Yang

2023

IEEE Access

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To improve the accuracy of graph neural network recommendation algorithms, research mainly integrates multi head attention mechanism and GRU to construct a graph neural network recommendation model; Considering the long and short term preferences of users, a graph neural network algorithm integrating long and short term preferences is constructed. The research results indicated that when the embedding dimension was 64, the batch size of selected samples was 64, the learning rate was 0.0005, the vertical stacking layer of GRU was 2, the iteration period was 5, and the dropout probability was 50% with the best performance. The graph neural network algorithm based on long and short term preferences had higher recommendation accuracy compared to other algorithms. Modeling users' short-term and long-term preferences can achieve the goal of comprehensively improving recommendation effectiveness.

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“…To address the shortcomings of the overall network random sampling prediction method, a relationship prediction method with random sampling in colleges and universities is proposed as the CCS prediction algorithm. [27] e CCS algorithm is applicable to networks where all college information is known and the college structures do not overlap. When a node belongs to more than one college at the same time, it is grouped into the college with the higher number of relationships among nodes.…”

Section: Ccs Algorithmmentioning

confidence: 99%

A Method to Study Group Relationship of College Students Based on Predictive Social Network

Zhang

Shi

2022

Scientific Programming

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With the rise of social network platforms such as WeChat, Weibo, and TikTok, social networks have developed from simple social networks to complex social networks. Researchers have gradually found that the traditional data sampling methods can no longer meet the development needs of the complex social network structure. In order to save network resources, various methods of social relationship prediction have been proposed. In this paper, we propose a BTCS algorithm based on low sampling rate under cognitive model and conduct several sets of comparison experiments under different networks and different sampling rates, and the results show that the BTCS algorithm improves the prediction accuracy and reduces the prediction time under low sampling rate. To address the problems of poor stability and slow prediction speed of random sampling prediction methods, this paper proposes a CCS algorithm in colleges and universities using the characteristics of high awareness among nodes within the same college. It can effectively combine the cognitive characteristics of the nodes with the college attributes and apply them to the relationship prediction to realize the college-oriented relationship prediction. The simulation results show that the CCS algorithm is more stable than other random sampling prediction methods. The results make full use of the cognitive characteristics and college attributes of nodes in social networks; reduce the influence of multiple factors such as response time, data packet loss, and individual behavior on relationship prediction; and improve the efficiency of college student group relationship prediction, which has certain theoretical significance and application prospects.

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Image Recommendation Algorithm Combined with Deep Neural Network Designed for Social Networks

Cited by 12 publications

References 39 publications

Book Classification and Recommendation for University Libraries using Grey Correlation and Bayesian Probability

Book Classification and Recommendation for University Libraries using Grey Correlation and Bayesian Probability

A Graph Neural Network Recommendation Method Integrating Multi Head Attention Mechanism and Improved Gated Recurrent Unit Algorithm

A Method to Study Group Relationship of College Students Based on Predictive Social Network

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