Image Recommendation Algorithm Combined with Deep Neural Network Designed for Social Networks
In recent years, deep neural networks have achieved great success in many fields, such as computer vision and natural language processing. Traditional image recommendation algorithms use text-based recommendation methods. The process of displaying images requires a lot of time and labor, and the tim...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2021-01-01
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| Series: | Complexity |
| Online Access: | http://dx.doi.org/10.1155/2021/5196190 |
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| _version_ | 1832561416722186240 |
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| author | Shaohui Du Zhenghan Chen Haoyan Wu Yihong Tang YuanQing Li |
| author_facet | Shaohui Du Zhenghan Chen Haoyan Wu Yihong Tang YuanQing Li |
| author_sort | Shaohui Du |
| collection | DOAJ |
| description | In recent years, deep neural networks have achieved great success in many fields, such as computer vision and natural language processing. Traditional image recommendation algorithms use text-based recommendation methods. The process of displaying images requires a lot of time and labor, and the time-consuming labor is inefficient. Therefore, this article mainly studies image recommendation algorithms based on deep neural networks in social networks. First, according to the time stamp information of the dataset, the interaction records of each user are sorted by the closest time. Then, some feature vectors are created via traditional feature algorithms like LBP, BGC3, RTU, or CNN extraction. For image recommendation, two LSTM neural networks are established, which accept these feature vectors as input, respectively. The compressed output of the two sub-ESTM neural networks is used as the input of another LSTM neural network. The multilayer regression algorithm is adopted to randomly sample some network nodes to obtain the cognitive information of the nodes sampled in the entire network, predict the relationship between all nodes in the network based on the cognitive information, and perform low sampling to achieve relationship prediction. The experiments show that proposed LSTM model together with CNN feature vectors can outperform other algorithms. |
| format | Article |
| id | doaj-art-377f97382545486686ef19ef3d987551 |
| institution | Kabale University |
| issn | 1076-2787 1099-0526 |
| language | English |
| publishDate | 2021-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Complexity |
| spelling | doaj-art-377f97382545486686ef19ef3d9875512025-02-03T01:25:01ZengWileyComplexity1076-27871099-05262021-01-01202110.1155/2021/51961905196190Image Recommendation Algorithm Combined with Deep Neural Network Designed for Social NetworksShaohui Du0Zhenghan Chen1Haoyan Wu2Yihong Tang3YuanQing Li4Saint-Petersburg State University, Saint-Petersburg, RussiaPerking University, Beijing, ChinaBeijing Jiaotong University, Beijing, ChinaNortheastern University at Qinhuangdao, Qinhuangdao, ChinaUniversity of Bristol, Bristol, UKIn recent years, deep neural networks have achieved great success in many fields, such as computer vision and natural language processing. Traditional image recommendation algorithms use text-based recommendation methods. The process of displaying images requires a lot of time and labor, and the time-consuming labor is inefficient. Therefore, this article mainly studies image recommendation algorithms based on deep neural networks in social networks. First, according to the time stamp information of the dataset, the interaction records of each user are sorted by the closest time. Then, some feature vectors are created via traditional feature algorithms like LBP, BGC3, RTU, or CNN extraction. For image recommendation, two LSTM neural networks are established, which accept these feature vectors as input, respectively. The compressed output of the two sub-ESTM neural networks is used as the input of another LSTM neural network. The multilayer regression algorithm is adopted to randomly sample some network nodes to obtain the cognitive information of the nodes sampled in the entire network, predict the relationship between all nodes in the network based on the cognitive information, and perform low sampling to achieve relationship prediction. The experiments show that proposed LSTM model together with CNN feature vectors can outperform other algorithms.http://dx.doi.org/10.1155/2021/5196190 |
| spellingShingle | Shaohui Du Zhenghan Chen Haoyan Wu Yihong Tang YuanQing Li Image Recommendation Algorithm Combined with Deep Neural Network Designed for Social Networks Complexity |
| title | Image Recommendation Algorithm Combined with Deep Neural Network Designed for Social Networks |
| title_full | Image Recommendation Algorithm Combined with Deep Neural Network Designed for Social Networks |
| title_fullStr | Image Recommendation Algorithm Combined with Deep Neural Network Designed for Social Networks |
| title_full_unstemmed | Image Recommendation Algorithm Combined with Deep Neural Network Designed for Social Networks |
| title_short | Image Recommendation Algorithm Combined with Deep Neural Network Designed for Social Networks |
| title_sort | image recommendation algorithm combined with deep neural network designed for social networks |
| url | http://dx.doi.org/10.1155/2021/5196190 |
| work_keys_str_mv | AT shaohuidu imagerecommendationalgorithmcombinedwithdeepneuralnetworkdesignedforsocialnetworks AT zhenghanchen imagerecommendationalgorithmcombinedwithdeepneuralnetworkdesignedforsocialnetworks AT haoyanwu imagerecommendationalgorithmcombinedwithdeepneuralnetworkdesignedforsocialnetworks AT yihongtang imagerecommendationalgorithmcombinedwithdeepneuralnetworkdesignedforsocialnetworks AT yuanqingli imagerecommendationalgorithmcombinedwithdeepneuralnetworkdesignedforsocialnetworks |