The short video platform recommendation mechanism based on the improved neural network algorithm to the mainstream media
Currently, the capacity of short videos continues to increase. Video manufacturers hope to enhance user experience and stickiness through recommendation algorithms, while users seek personalized videos to save time and money. Therefore, in order to address the data sparsity and high-dimensional feat...
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| Format: | Article |
| Language: | English |
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Elsevier
2024-12-01
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| Series: | Systems and Soft Computing |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2772941924001005 |
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| author | Mengruo Qi |
| author_facet | Mengruo Qi |
| author_sort | Mengruo Qi |
| collection | DOAJ |
| description | Currently, the capacity of short videos continues to increase. Video manufacturers hope to enhance user experience and stickiness through recommendation algorithms, while users seek personalized videos to save time and money. Therefore, in order to address the data sparsity and high-dimensional feature extraction, this study proposes a novel short video platform recommendation model. The proposed method utilizes the term frequency inverse document frequency algorithm for text mining, and combines error back propagation neural network for learning to explore the potential connection between users and videos. This method combines natural language processing and image analysis in deep learning to construct accurate user and video models, deeply explore user interests, and improve the accuracy and effectiveness of recommendation systems for user preferences. The research results showed that the recommendation accuracy of this method was 66 % and 70 % respectively, and the prediction accuracy was 73.50 % and 88 % respectively. When Num = 128, 200 data points were recommended within 0.3678 s. The proposed algorithm outperforms the other three methods in terms of recommendation accuracy compared with the ItemCF and UserCF algorithms. This is because the method uses an approach based on image and user vector, combined with relevant features of the video and user. The proposed method can deeply explore the relevant features of videos and users, overcoming the data scarcity in previous collaborative screening, and guiding video recommendation on practical media platforms. |
| format | Article |
| id | doaj-art-c5a9309035f14fd2a1ce6961a66c7489 |
| institution | Kabale University |
| issn | 2772-9419 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Systems and Soft Computing |
| spelling | doaj-art-c5a9309035f14fd2a1ce6961a66c74892024-12-19T11:03:17ZengElsevierSystems and Soft Computing2772-94192024-12-016200171The short video platform recommendation mechanism based on the improved neural network algorithm to the mainstream mediaMengruo Qi0College of Chinese and ASEAN Arts, Chengdu University, Chengdu 610106, ChinaCurrently, the capacity of short videos continues to increase. Video manufacturers hope to enhance user experience and stickiness through recommendation algorithms, while users seek personalized videos to save time and money. Therefore, in order to address the data sparsity and high-dimensional feature extraction, this study proposes a novel short video platform recommendation model. The proposed method utilizes the term frequency inverse document frequency algorithm for text mining, and combines error back propagation neural network for learning to explore the potential connection between users and videos. This method combines natural language processing and image analysis in deep learning to construct accurate user and video models, deeply explore user interests, and improve the accuracy and effectiveness of recommendation systems for user preferences. The research results showed that the recommendation accuracy of this method was 66 % and 70 % respectively, and the prediction accuracy was 73.50 % and 88 % respectively. When Num = 128, 200 data points were recommended within 0.3678 s. The proposed algorithm outperforms the other three methods in terms of recommendation accuracy compared with the ItemCF and UserCF algorithms. This is because the method uses an approach based on image and user vector, combined with relevant features of the video and user. The proposed method can deeply explore the relevant features of videos and users, overcoming the data scarcity in previous collaborative screening, and guiding video recommendation on practical media platforms.http://www.sciencedirect.com/science/article/pii/S2772941924001005Deep neural networkNatural language processingCollaborative filteringShort videoUser recommendation model |
| spellingShingle | Mengruo Qi The short video platform recommendation mechanism based on the improved neural network algorithm to the mainstream media Systems and Soft Computing Deep neural network Natural language processing Collaborative filtering Short video User recommendation model |
| title | The short video platform recommendation mechanism based on the improved neural network algorithm to the mainstream media |
| title_full | The short video platform recommendation mechanism based on the improved neural network algorithm to the mainstream media |
| title_fullStr | The short video platform recommendation mechanism based on the improved neural network algorithm to the mainstream media |
| title_full_unstemmed | The short video platform recommendation mechanism based on the improved neural network algorithm to the mainstream media |
| title_short | The short video platform recommendation mechanism based on the improved neural network algorithm to the mainstream media |
| title_sort | short video platform recommendation mechanism based on the improved neural network algorithm to the mainstream media |
| topic | Deep neural network Natural language processing Collaborative filtering Short video User recommendation model |
| url | http://www.sciencedirect.com/science/article/pii/S2772941924001005 |
| work_keys_str_mv | AT mengruoqi theshortvideoplatformrecommendationmechanismbasedontheimprovedneuralnetworkalgorithmtothemainstreammedia AT mengruoqi shortvideoplatformrecommendationmechanismbasedontheimprovedneuralnetworkalgorithmtothemainstreammedia |