| Autor: |
Gongcai Wu |
| Jazyk: |
angličtina |
| Rok vydání: |
2024 |
| Předmět: |
|
| Zdroj: |
Electronic Research Archive, Vol 32, Iss 7, Pp 4378-4397 (2024) |
| Druh dokumentu: |
article |
| ISSN: |
2688-1594 |
| DOI: |
10.3934/era.2024197?viewType=HTML |
| Popis: |
In recent years, the vigorous development of the Internet has led to the exponential growth of network information. The recommendation system can analyze the potential preferences of users according to their historical behavior data and provide personalized recommendations for users. In this study, the social network model was used for modeling, and the recommendation model was improved based on variational modal decomposition and the whale optimization algorithm. The generalized regression neural network structure and joint probability density function were used for sequencing and optimization, and then the genetic bat population optimization algorithm was used to solve the proposed algorithm. An intelligent recommendation algorithm for social networks based on improved generalized regression neural networks (RA-GNN) was proposed. In this study, three kinds of social network data sets obtained by real crawlers were used to solve the proposed RA-GNN algorithm in the real social network data environment. The experimental results showed that the RA-GNN algorithm proposed in this paper could implement efficient and accurate recommendations for social network information. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
|
|
Nepřihlášeným uživatelům se plný text nezobrazuje |
K zobrazení výsledku je třeba se přihlásit.
|
