{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,13]],"date-time":"2026-01-13T21:59:49Z","timestamp":1768341589296,"version":"3.49.0"},"reference-count":60,"publisher":"Springer Science and Business Media LLC","issue":"11","license":[{"start":{"date-parts":[[2023,9,18]],"date-time":"2023-09-18T00:00:00Z","timestamp":1694995200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,9,18]],"date-time":"2023-09-18T00:00:00Z","timestamp":1694995200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Multimed Tools Appl"],"DOI":"10.1007\/s11042-023-16898-2","type":"journal-article","created":{"date-parts":[[2023,9,18]],"date-time":"2023-09-18T11:01:47Z","timestamp":1695034907000},"page":"31629-31653","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["ADOSMNet: a novel visual affordance detection network with object shape mask guided feature encoders"],"prefix":"10.1007","volume":"83","author":[{"given":"Dongpan","family":"Chen","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7722-7172","authenticated-orcid":false,"given":"Dehui","family":"Kong","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jinghua","family":"Li","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shaofan","family":"Wang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Baocai","family":"Yin","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2023,9,18]]},"reference":[{"key":"16898_CR1","first-page":"44","volume":"2","author":"JJ Gibson","year":"1966","unstructured":"Gibson JJ, Carmichael L (1966) The Senses Considered as Perceptual Systems 2:44\u201373","journal-title":"The Senses Considered as Perceptual Systems"},{"issue":"2","key":"16898_CR2","first-page":"67","volume":"1","author":"JJ Gibson","year":"1977","unstructured":"Gibson JJ (1977) The theory of affordances. Hilldale, USA 1(2):67\u201382","journal-title":"Hilldale, USA"},{"key":"16898_CR3","doi-asserted-by":"publisher","DOI":"10.1016\/j.rcim.2022.102360","volume":"77","author":"Z Liu","year":"2022","unstructured":"Liu Z, Liu Q, Xu W, Wang L, Zhou Z (2022) Robot learning towards smart robotic manufacturing: A review. Robot Comput Integr Manuf 77:102360","journal-title":"Robot Comput Integr Manuf"},{"key":"16898_CR4","doi-asserted-by":"publisher","first-page":"31282","DOI":"10.1109\/ACCESS.2023.3262450","volume":"11","author":"F Munguia-Galeano","year":"2023","unstructured":"Munguia-Galeano F, Veeramani S, Hern\u00e1ndez JD, Wen Q, Ji Z (2023) Affordance-based human-robot interaction with reinforcement learning. IEEE Access 11:31282\u201331292","journal-title":"IEEE Access"},{"key":"16898_CR5","doi-asserted-by":"crossref","unstructured":"Wu, Y-H, Liu, Y, Zhan, X, Cheng, M-M (2022) P2t: Pyramid pooling transformer for scene understanding. IEEE Transactions on Pattern Analysis and Machine Intelligence, pp 1\u201312","DOI":"10.1109\/TPAMI.2022.3202765"},{"key":"16898_CR6","doi-asserted-by":"crossref","unstructured":"Hou, Z, Yu, B, Qiao, Y, Peng, X, Tao, D (2021) Affordance transfer learning for human-object interaction detection. In: IEEE conference on computer vision and pattern recognition, pp 495\u2013504","DOI":"10.1109\/CVPR46437.2021.00056"},{"key":"16898_CR7","doi-asserted-by":"crossref","unstructured":"Shao, D, Zhao, Y, Dai, B, Lin, D (2020) Finegym: A hierarchical video dataset for fine-grained action understanding. In: IEEE conference on computer vision and pattern recognition, pp 2616\u20132625","DOI":"10.1109\/CVPR42600.2020.00269"},{"issue":"6","key":"16898_CR8","doi-asserted-by":"publisher","first-page":"4755","DOI":"10.1007\/s10462-021-10116-x","volume":"55","author":"N Gupta","year":"2022","unstructured":"Gupta N, Gupta SK, Pathak RK, Jain V, Rashidi P, Suri JS (2022) Human activity recognition in artificial intelligence framework: A narrative review. Artif Intell Rev 55(6):4755\u20134808","journal-title":"Artif Intell Rev"},{"key":"16898_CR9","doi-asserted-by":"crossref","unstructured":"Srivastava, Y, Murali, V, Dubey, SR, Mukherjee, S (2021) Visual question answering using deep learning: A survey and performance analysis. In: Computer vision and image processing: 5th international conference, CVIP 2020, Prayagraj, India, December 4-6, 2020, Revised Selected Papers, Part II 5, pp 75\u201386","DOI":"10.1007\/978-981-16-1092-9_7"},{"key":"16898_CR10","doi-asserted-by":"crossref","unstructured":"Chen, L, Zheng, Y, Xiao, J (2022) Rethinking data augmentation for robust visual question answering. In: European conference on computer vision, pp 95\u2013112","DOI":"10.1007\/978-3-031-20059-5_6"},{"key":"16898_CR11","doi-asserted-by":"crossref","unstructured":"Roy, A, Todorovic, S (2016) A multi-scale cnn for affordance segmentation in rgb images. In: European conference on computer vision, pp 186\u2013201","DOI":"10.1007\/978-3-319-46493-0_12"},{"key":"16898_CR12","doi-asserted-by":"crossref","unstructured":"Cao, Y, Xu, J, Lin, S, Wei, F, Hu, H (2019) Gcnet: Non-local networks meet squeeze-excitation networks and beyond. In: IEEE international conference on computer vision, pp 0\u20130","DOI":"10.1109\/ICCVW.2019.00246"},{"key":"16898_CR13","doi-asserted-by":"publisher","first-page":"36","DOI":"10.1016\/j.neucom.2021.01.018","volume":"440","author":"Q Gu","year":"2021","unstructured":"Gu Q, Su J, Yuan L (2021) Visual affordance detection using an efficient attention convolutional neural network. Neurocomputing 440:36\u201344","journal-title":"Neurocomputing"},{"key":"16898_CR14","unstructured":"Minh, CND, Gilani, SZ, Islam, SMS, Suter, D (2020) Learning affordance segmentation: An investigative study. In: 2020 Digital image computing: techniques and applications, pp 1\u20138"},{"key":"16898_CR15","unstructured":"Lu, L, Zhai, W, Luo, H, Kang, Y, Cao, Y (2022) Phrase-based affordance detection via cyclic bilateral interaction. IEEE Transactions on Artificial Intelligence, pp 1\u201313"},{"key":"16898_CR16","doi-asserted-by":"crossref","unstructured":"Nguyen, A, Kanoulas, D, Caldwell, DG, Tsagarakis, NG (2017) Object-based affordances detection with convolutional neural networks and dense conditional random fields. In: 2017 IEEE\/RSJ international conference on intelligent robots and systems, pp 5908\u20135915","DOI":"10.1109\/IROS.2017.8206484"},{"key":"16898_CR17","doi-asserted-by":"crossref","unstructured":"Do, T-T, Nguyen, A, Reid, I (2018) Affordancenet: An end-to-end deep learning approach for object affordance detection. In: 2018 IEEE international conference on robotics and automation, pp 5882\u20135889","DOI":"10.1109\/ICRA.2018.8460902"},{"key":"16898_CR18","first-page":"91","volume":"28","author":"S Ren","year":"2015","unstructured":"Ren S, He K, Girshick R, Sun J (2015) Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28:91\u201399","journal-title":"Advances in neural information processing systems"},{"key":"16898_CR19","doi-asserted-by":"crossref","unstructured":"Zhao, H, Shi, J, Qi, X, Wang, X, Jia, J (2017) Pyramid scene parsing network. In: IEEE conference on computer vision and pattern recognition, pp 2881\u20132890","DOI":"10.1109\/CVPR.2017.660"},{"issue":"7","key":"16898_CR20","doi-asserted-by":"publisher","first-page":"4499","DOI":"10.1007\/s11042-019-7684-3","volume":"79","author":"F Fooladgar","year":"2020","unstructured":"Fooladgar F, Kasaei S (2020) A survey on indoor rgb-d semantic segmentation: from hand-crafted features to deep convolutional neural networks. Multimedia Tools and Applications 79(7):4499\u20134524","journal-title":"Multimedia Tools and Applications"},{"key":"16898_CR21","doi-asserted-by":"crossref","unstructured":"Tang, Y, Zhang, C, Cheng, Q, Li, Z, Qian, L (2022) Fast semantic segmentation network with attention gate and multi-layer fusion. Multimedia Tools and Applications, pp 1\u201316","DOI":"10.1007\/s11042-022-12519-6"},{"issue":"14","key":"16898_CR22","doi-asserted-by":"publisher","first-page":"21771","DOI":"10.1007\/s11042-021-10510-1","volume":"80","author":"NU Haq","year":"2021","unstructured":"Haq NU, Khan A, Din A, Shao L, Shah S et al (2021) A novel weight initialization with adaptive hyper-parameters for deep semantic segmentation. Multimedia Tools and Applications 80(14):21771\u201321787","journal-title":"Multimedia Tools and Applications"},{"key":"16898_CR23","unstructured":"Yuan, X, Liu, C, Feng, F, Zhu, Y, Wang, Y (2022) Slice-mask based 3d cardiac shape reconstruction from ct volume. In: Proceedings of the asian conference on computer vision, pp 1909\u20131925"},{"key":"16898_CR24","doi-asserted-by":"crossref","unstructured":"Sun, J, Chen, L, Xie, Y, Zhang, S, Jiang, Q, Zhou, X, Bao, H (2020) Disp r-cnn: Stereo 3d object detection via shape prior guided instance disparity estimation. In: IEEE conference on computer vision and pattern recognition, pp 10548\u201310557","DOI":"10.1109\/CVPR42600.2020.01056"},{"key":"16898_CR25","doi-asserted-by":"crossref","unstructured":"Myers, A, Teo, C.L, Ferm\u00fcller, C, Aloimonos, Y (2015) Affordance detection of tool parts from geometric features. In: 2015 IEEE international conference on robotics and automation, pp 1374\u20131381","DOI":"10.1109\/ICRA.2015.7139369"},{"key":"16898_CR26","unstructured":"Hermans, T, Rehg, JM, Bobick, A (2011) Affordance prediction via learned object attributes. In: IEEE international conference on robotics and automation: workshop on semantic perception, mapping, and exploration, pp 181\u2013184"},{"issue":"1","key":"16898_CR27","doi-asserted-by":"publisher","first-page":"81","DOI":"10.1016\/j.cviu.2010.08.002","volume":"115","author":"H Kjellstr\u00f6m","year":"2011","unstructured":"Kjellstr\u00f6m H, Romero J, Kragi\u0107 D (2011) Visual object-action recognition: Inferring object affordances from human demonstration. Comput Vis Image Underst 115(1):81\u201390","journal-title":"Comput Vis Image Underst"},{"issue":"8","key":"16898_CR28","doi-asserted-by":"publisher","first-page":"951","DOI":"10.1177\/0278364913478446","volume":"32","author":"HS Koppula","year":"2013","unstructured":"Koppula HS, Gupta R, Saxena A (2013) Learning human activities and object affordances from rgb-d videos. The International Journal of Robotics Research 32(8):951\u2013970","journal-title":"The International Journal of Robotics Research"},{"key":"16898_CR29","doi-asserted-by":"crossref","unstructured":"He, K, Gkioxari, G, Doll\u00e1r, P, Girshick, R (2017) Mask r-cnn. In: IEEE international conference on computer vision, pp 2961\u20132969","DOI":"10.1109\/ICCV.2017.322"},{"issue":"16","key":"16898_CR30","doi-asserted-by":"publisher","first-page":"23473","DOI":"10.1007\/s11042-022-12584-x","volume":"81","author":"A Bastanfard","year":"2022","unstructured":"Bastanfard A, Amirkhani D, Mohammadi M (2022) Toward image super-resolution based on local regression and nonlocal means. Multimedia Tools and Applications 81(16):23473\u201323492","journal-title":"Multimedia Tools and Applications"},{"issue":"18","key":"16898_CR31","doi-asserted-by":"publisher","first-page":"14321","DOI":"10.1007\/s00521-019-04336-0","volume":"32","author":"X Zhao","year":"2020","unstructured":"Zhao X, Cao Y, Kang Y (2020) Object affordance detection with relationship-aware network. Neural Comput & Applic 32(18):14321\u201314333","journal-title":"Neural Comput & Applic"},{"key":"16898_CR32","doi-asserted-by":"crossref","unstructured":"Sawatzky, J, Gall, J (2017) Adaptive binarization for weakly supervised affordance segmentation. In: IEEE international conference on computer vision, pp 1383\u20131391","DOI":"10.1109\/CVPR.2017.552"},{"issue":"2","key":"16898_CR33","doi-asserted-by":"publisher","first-page":"1140","DOI":"10.1109\/LRA.2019.2894439","volume":"4","author":"F-J Chu","year":"2019","unstructured":"Chu F-J, Xu R, Vela PA (2019) Learning affordance segmentation for real-world robotic manipulation via synthetic images. IEEE Robotics and Automation Letters 4(2):1140\u20131147","journal-title":"IEEE Robotics and Automation Letters"},{"key":"16898_CR34","doi-asserted-by":"crossref","unstructured":"Deng, S, Xu, X, Wu, C, Chen, K, Jia, K (2021) 3d affordancenet: A benchmark for visual object affordance understanding. In: IEEE conference on computer vision and pattern recognition, pp 1778\u20131787","DOI":"10.1109\/CVPR46437.2021.00182"},{"key":"16898_CR35","doi-asserted-by":"crossref","unstructured":"Mo, K, Zhu, S, Chang, AX, Yi, L, Tripathi, S, Guibas, LJ, Su, H (2019) Partnet: A large-scale benchmark for fine-grained and hierarchical part-level 3d object understanding. In: IEEE conference on computer vision and pattern recognition, pp 909\u2013918","DOI":"10.1109\/CVPR.2019.00100"},{"key":"16898_CR36","unstructured":"Chang, A.X, Funkhouser, T, Guibas, L, Hanrahan, P, Huang, Q, Li, Z, Savarese, S, Savva, M, Song, S, Su, H, et al (2015) Shapenet: An information-rich 3d model repository. In: arXiv:1512.03012"},{"key":"16898_CR37","unstructured":"Xu, C, Chen, Y, Wang, H, Zhu, S-C, Zhu, Y, Huang, S (2022) Partafford: Part-level affordance discovery from 3d objects. arXiv:2202.13519"},{"key":"16898_CR38","doi-asserted-by":"crossref","unstructured":"Lun, Z, Gadelha, M, Kalogerakis, E, Maji, S, Wang, R (2017) 3d shape reconstruction from sketches via multi-view convolutional networks. In: 2017 International conference on 3D vision, pp 67\u201377","DOI":"10.1109\/3DV.2017.00018"},{"issue":"3","key":"16898_CR39","doi-asserted-by":"publisher","first-page":"1466","DOI":"10.1109\/TVCG.2018.2871190","volume":"26","author":"X Chen","year":"2018","unstructured":"Chen X, Li Y, Luo X, Shao T, Yu J, Zhou K, Zheng Y (2018) Autosweep: Recovering 3d editable objects from a single photograph. IEEE Trans Vis Comput Graph 26(3):1466\u20131475","journal-title":"IEEE Trans Vis Comput Graph"},{"key":"16898_CR40","doi-asserted-by":"crossref","unstructured":"Wimbauer, F, Yang, N, von Stumberg, L, Zeller, N, Cremers, D (2021) Monorec: Semi-supervised dense reconstruction in dynamic environments from a single moving camera. In: IEEE conference on computer vision and pattern recognition, pp 6112\u20136122","DOI":"10.1109\/CVPR46437.2021.00605"},{"issue":"9","key":"16898_CR41","doi-asserted-by":"publisher","first-page":"3518","DOI":"10.1109\/TCSVT.2020.3040900","volume":"31","author":"Y Zhong","year":"2020","unstructured":"Zhong Y, Qi Y, Gryaditskaya Y, Zhang H, Song Y-Z (2020) Towards practical sketch-based 3d shape generation: The role of professional sketches. IEEE Transactions on Circuits and Systems for Video Technology 31(9):3518\u20133528","journal-title":"IEEE Transactions on Circuits and Systems for Video Technology"},{"issue":"3","key":"16898_CR42","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3443708","volume":"17","author":"J Nie","year":"2021","unstructured":"Nie J, Wei Z-Q, Nie W, Liu A-A (2021) Pgnet: Progressive feature guide learning network for three-dimensional shape recognition. ACM Trans Multimed Comput Commun Appl 17(3):1\u201317","journal-title":"ACM Trans Multimed Comput Commun Appl"},{"key":"16898_CR43","doi-asserted-by":"crossref","unstructured":"Ding, H, Jiang, X, Shuai, B, Liu, AQ, Wang, G (2019) Semantic correlation promoted shape-variant context for segmentation. In: IEEE conference on computer vision and pattern recognition, pp 8885\u20138894","DOI":"10.1109\/CVPR.2019.00909"},{"key":"16898_CR44","doi-asserted-by":"crossref","unstructured":"Kuo, W, Angelova, A, Malik, J, Lin, T-Y (2019) Shapemask: Learning to segment novel objects by refining shape priors. In: IEEE international conference on computer vision, pp 9207\u20139216","DOI":"10.1109\/ICCV.2019.00930"},{"issue":"17","key":"16898_CR45","doi-asserted-by":"publisher","first-page":"26199","DOI":"10.1007\/s11042-021-10883-3","volume":"80","author":"D Amirkhani","year":"2021","unstructured":"Amirkhani D, Bastanfard A (2021) An objective method to evaluate exemplar-based inpainted images quality using jaccard index. Multimedia Tools and Applications 80(17):26199\u201326212","journal-title":"Multimedia Tools and Applications"},{"issue":"8","key":"16898_CR46","doi-asserted-by":"publisher","first-page":"2663","DOI":"10.1109\/TCSVT.2019.2924912","volume":"30","author":"X Wang","year":"2020","unstructured":"Wang X, Shen C, Li H, Xu S (2020) Human detection aided by deeply learned semantic masks. IEEE Trans. Circuits Syst Video Technol 30(8):2663\u20132673","journal-title":"IEEE Trans. Circuits Syst Video Technol"},{"issue":"9","key":"16898_CR47","doi-asserted-by":"publisher","first-page":"3119","DOI":"10.1109\/TCSVT.2019.2934989","volume":"30","author":"S Jiang","year":"2020","unstructured":"Jiang S, Lu X, Lei Y, Liu L (2020) Mask-aware networks for crowd counting. IEEE Transactions on Circuits and Systems for Video Technology 30(9):3119\u20133129","journal-title":"IEEE Transactions on Circuits and Systems for Video Technology"},{"issue":"1","key":"16898_CR48","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3467889","volume":"18","author":"A Mao","year":"2022","unstructured":"Mao A, Liang Y, Jiao J, Liu Y, He S (2022) Mask-guided deformation adaptive network for human parsing. ACM Trans Multimed Comput Commun Appl 18(1):1\u201320","journal-title":"ACM Trans Multimed Comput Commun Appl"},{"issue":"3","key":"16898_CR49","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3391290","volume":"16","author":"X Wang","year":"2020","unstructured":"Wang X, Tian Y, Zhao X, Yang T, Gelernter J, Wang J, Cheng G, Hu W (2020) Improving multiperson pose estimation by mask-aware deep reinforcement learning. ACM Transactions on Multimedia Computing, Communications, and Applications 16(3):1\u201318","journal-title":"ACM Transactions on Multimedia Computing, Communications, and Applications"},{"key":"16898_CR50","doi-asserted-by":"crossref","unstructured":"Chen, L-C, Zhu, Y, Papandreou, G, Schroff, F, Adam, H (2018) Encoder-decoder with atrous separable convolution for semantic image segmentation. In: Proceedings of the european conference on computer vision, pp 801\u2013818","DOI":"10.1007\/978-3-030-01234-2_49"},{"issue":"3","key":"16898_CR51","doi-asserted-by":"publisher","first-page":"211","DOI":"10.1007\/s11263-015-0816-y","volume":"115","author":"O Russakovsky","year":"2015","unstructured":"Russakovsky O, Deng J, Su H, Krause J, Satheesh S, Ma S, Huang Z, Karpathy A, Khosla A, Bernstein M et al (2015) Imagenet large scale visual recognition challenge. Int J Comput Vis 115(3):211\u2013252","journal-title":"Int J Comput Vis"},{"key":"16898_CR52","doi-asserted-by":"crossref","unstructured":"Ronneberger, O, Fischer, P, Brox, T (2015) U-net: Convolutional networks for biomedical image segmentation. In: International conference on medical image computing and computer-assisted intervention, pp 234\u2013241","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"16898_CR53","doi-asserted-by":"crossref","unstructured":"Long, J, Shelhamer, E, Darrell, T (2015) Fully convolutional networks for semantic segmentation. In: IEEE conference on computer vision and pattern recognition, pp 3431\u20133440","DOI":"10.1109\/CVPR.2015.7298965"},{"key":"16898_CR54","doi-asserted-by":"crossref","unstructured":"Wang, X, Girshick, R, Gupta, A, He, K (2018) Non-local neural networks. In: IEEE conference on computer vision and pattern recognition, pp 7794\u20137803","DOI":"10.1109\/CVPR.2018.00813"},{"key":"16898_CR55","unstructured":"Chen, L-C, Papandreou, G, Schroff, F, Adam, H (2017) Rethinking atrous convolution for semantic image segmentation. arXiv:1706.05587"},{"key":"16898_CR56","doi-asserted-by":"crossref","unstructured":"Nguyen, A, Kanoulas, D, Caldwell, DG, Tsagarakis, NG (2016) Detecting object affordances with convolutional neural networks. In: 2016 IEEE\/RSJ international conference on intelligent robots and systems, pp 2765\u20132770","DOI":"10.1109\/IROS.2016.7759429"},{"issue":"4","key":"16898_CR57","doi-asserted-by":"publisher","first-page":"834","DOI":"10.1109\/TPAMI.2017.2699184","volume":"40","author":"L-C Chen","year":"2017","unstructured":"Chen L-C, Papandreou G, Kokkinos I, Murphy K, Yuille AL (2017) Deeplab: Semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected crfs. IEEE Transactions on Pattern Analysis and Machine Intelligence 40(4):834\u2013848","journal-title":"IEEE Transactions on Pattern Analysis and Machine Intelligence"},{"issue":"20","key":"16898_CR58","doi-asserted-by":"publisher","first-page":"17963","DOI":"10.1007\/s00521-022-07446-4","volume":"34","author":"C Yin","year":"2022","unstructured":"Yin C, Zhang Q (2022) Object affordance detection with boundary-preserving network for robotic manipulation tasks. Neural Comput & Applic 34(20):17963\u201317980","journal-title":"Neural Comput & Applic"},{"key":"16898_CR59","doi-asserted-by":"crossref","unstructured":"Zhang, Y, Li, H, Ren, T, Dou, Y, Li, Q (2022) Multi-scale fusion and global semantic encoding for affordance detection. In: 2022 International joint conference on neural networks, pp 1\u20138","DOI":"10.1109\/IJCNN55064.2022.9892363"},{"key":"16898_CR60","doi-asserted-by":"crossref","unstructured":"Zheng, G, Zhang, F, Zheng, Z, Xiang, Y, Yuan, NJ, Xie, X, Li, Z (2018) Drn: A deep reinforcement learning framework for news recommendation. In: The 2018 WWW Conference, pp 167\u2013176","DOI":"10.1145\/3178876.3185994"}],"container-title":["Multimedia Tools and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11042-023-16898-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11042-023-16898-2\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11042-023-16898-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,3,8]],"date-time":"2024-03-08T06:42:20Z","timestamp":1709880140000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11042-023-16898-2"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,9,18]]},"references-count":60,"journal-issue":{"issue":"11","published-online":{"date-parts":[[2024,3]]}},"alternative-id":["16898"],"URL":"https:\/\/doi.org\/10.1007\/s11042-023-16898-2","relation":{},"ISSN":["1573-7721"],"issn-type":[{"value":"1573-7721","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,9,18]]},"assertion":[{"value":"15 July 2022","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"18 May 2023","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"4 September 2023","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"18 September 2023","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare that they have no competing financial interests in the subject matter or materials discussed in this paper.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflicts of interest"}}]}}