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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\u2010ESTM 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.<\/jats:p>","DOI":"10.1155\/2021\/5196190","type":"journal-article","created":{"date-parts":[[2021,7,2]],"date-time":"2021-07-02T23:38:52Z","timestamp":1625269132000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":11,"title":["Image Recommendation Algorithm Combined with Deep Neural Network Designed for Social Networks"],"prefix":"10.1155","volume":"2021","author":[{"given":"Shaohui","family":"Du","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1841-539X","authenticated-orcid":false,"given":"Zhenghan","family":"Chen","sequence":"additional","affiliation":[]},{"given":"Haoyan","family":"Wu","sequence":"additional","affiliation":[]},{"given":"Yihong","family":"Tang","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7803-6439","authenticated-orcid":false,"given":"YuanQing","family":"Li","sequence":"additional","affiliation":[]}],"member":"311","published-online":{"date-parts":[[2021,7,2]]},"reference":[{"key":"e_1_2_9_1_2","doi-asserted-by":"publisher","DOI":"10.1088\/1742-5468\/2014\/06\/p06006"},{"key":"e_1_2_9_2_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.amc.2014.12.035"},{"key":"e_1_2_9_3_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.amc.2015.08.008"},{"key":"e_1_2_9_4_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.physa.2014.12.006"},{"key":"e_1_2_9_5_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2015.04.065"},{"key":"e_1_2_9_6_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.dam.2015.07.001"},{"key":"e_1_2_9_7_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.dam.2015.09.006"},{"key":"e_1_2_9_8_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.amc.2016.06.017"},{"key":"e_1_2_9_9_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2015.06.109"},{"key":"e_1_2_9_10_2","doi-asserted-by":"publisher","DOI":"10.1080\/03081087.2016.1179249"},{"key":"e_1_2_9_11_2","doi-asserted-by":"publisher","DOI":"10.1186\/s12874-018-0482-1"},{"key":"e_1_2_9_12_2","doi-asserted-by":"publisher","DOI":"10.1109\/taslp.2015.2496226"},{"key":"e_1_2_9_13_2","doi-asserted-by":"publisher","DOI":"10.1145\/3007787.3001165"},{"key":"e_1_2_9_14_2","doi-asserted-by":"publisher","DOI":"10.1109\/tvlsi.2017.2654298"},{"key":"e_1_2_9_15_2","doi-asserted-by":"crossref","DOI":"10.1371\/journal.pone.0155781","article-title":"Intrusion detection system using deep neural network for in-vehicle network security","volume":"11","author":"Min-Joo K.","year":"2016","journal-title":"PLoS One"},{"key":"e_1_2_9_16_2","doi-asserted-by":"publisher","DOI":"10.1109\/TITS.2017.2749965"},{"key":"e_1_2_9_17_2","doi-asserted-by":"publisher","DOI":"10.1039\/c7mb00188f"},{"key":"e_1_2_9_18_2","doi-asserted-by":"publisher","DOI":"10.1109\/tpds.2016.2626289"},{"key":"e_1_2_9_19_2","article-title":"A Hybrid spectral clustering and deep neural network ensemble algorithm for intrusion detection in sensor networks","volume":"16","author":"Tao M.","year":"2016","journal-title":"Sensors"},{"key":"e_1_2_9_20_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.engappai.2016.01.032"},{"key":"e_1_2_9_21_2","doi-asserted-by":"publisher","DOI":"10.1063\/1.5011181"},{"key":"e_1_2_9_22_2","doi-asserted-by":"publisher","DOI":"10.1109\/tip.2018.2832296"},{"key":"e_1_2_9_23_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.csl.2015.05.005"},{"key":"e_1_2_9_24_2","doi-asserted-by":"publisher","DOI":"10.1186\/s41044-018-0031-2"},{"key":"e_1_2_9_25_2","doi-asserted-by":"publisher","DOI":"10.1145\/2827872"},{"key":"e_1_2_9_26_2","unstructured":"https:\/\/www.yelp.com\/dataset."},{"key":"e_1_2_9_27_2","doi-asserted-by":"publisher","DOI":"10.1504\/ijdmb.2016.076534"},{"key":"e_1_2_9_28_2","doi-asserted-by":"publisher","DOI":"10.1021\/acs.analchem.7b03795"},{"key":"e_1_2_9_29_2","doi-asserted-by":"publisher","DOI":"10.3901\/JME.2016.09.065"},{"key":"e_1_2_9_30_2","first-page":"325","article-title":"Deep-neural network approaches for speech recognition with heterogeneous groups of speakers including children","volume":"23","author":"Romain Seriz E. 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