{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,6,19]],"date-time":"2025-06-19T05:01:25Z","timestamp":1750309285718,"version":"3.41.0"},"publisher-location":"New York, NY, USA","reference-count":24,"publisher":"ACM","license":[{"start":{"date-parts":[[2023,5,5]],"date-time":"2023-05-05T00:00:00Z","timestamp":1683244800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2023,5,5]]},"DOI":"10.1145\/3589462.3589495","type":"proceedings-article","created":{"date-parts":[[2023,5,26]],"date-time":"2023-05-26T22:07:36Z","timestamp":1685138856000},"page":"177-184","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":1,"title":["Exploiting Deep Learning and Explanation Methods for Movie Tag Prediction"],"prefix":"10.1145","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4670-8157","authenticated-orcid":false,"given":"Erica","family":"Coppolillo","sequence":"first","affiliation":[{"name":"University of Calabria, Italy"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7711-9833","authenticated-orcid":false,"given":"Massimo","family":"Guarascio","sequence":"additional","affiliation":[{"name":"ICAR-CNR, Italy"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9641-8916","authenticated-orcid":false,"given":"Marco","family":"Minici","sequence":"additional","affiliation":[{"name":"Universit\u00e0 degli Studi di Pisa, Italy"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2922-0835","authenticated-orcid":false,"given":"Francesco Sergio","family":"Pisani","sequence":"additional","affiliation":[{"name":"ICAR-CNR, Italy"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2023,5,26]]},"reference":[{"key":"e_1_3_2_1_1_1","volume-title":"Manuel Montes-y G\u00f3mez, and Fabio\u00a0A Gonz\u00e1lez","author":"Arevalo John","year":"2017","unstructured":"John Arevalo , Thamar Solorio , Manuel Montes-y G\u00f3mez, and Fabio\u00a0A Gonz\u00e1lez . 2017 . Gated multimodal units for information fusion. arXiv preprint arXiv:1702.01992 (2017). John Arevalo, Thamar Solorio, Manuel Montes-y G\u00f3mez, and Fabio\u00a0A Gonz\u00e1lez. 2017. Gated multimodal units for information fusion. arXiv preprint arXiv:1702.01992 (2017)."},{"key":"e_1_3_2_1_2_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00949"},{"key":"e_1_3_2_1_3_1","volume-title":"BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding","author":"Devlin J.","year":"2019","unstructured":"J. Devlin , M.\u00a0 W. Chang , K. Lee , and K. Toutanova . 2019 . BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding . In NAACL-HLT. Association for Computational Linguistics , 4171\u20134186. https:\/\/doi.org\/10.18653\/v1\/N19-1423 10.18653\/v1 J. Devlin, M.\u00a0W. Chang, K. Lee, and K. Toutanova. 2019. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. In NAACL-HLT. Association for Computational Linguistics, 4171\u20134186. https:\/\/doi.org\/10.18653\/v1\/N19-1423"},{"key":"e_1_3_2_1_4_1","volume-title":"Rethinking movie genre classification with fine-grained semantic clustering. arXiv preprint arXiv:2012.02639","author":"Fish Edward","year":"2020","unstructured":"Edward Fish , Jon Weinbren , and Andrew Gilbert . 2020. Rethinking movie genre classification with fine-grained semantic clustering. arXiv preprint arXiv:2012.02639 ( 2020 ). Edward Fish, Jon Weinbren, and Andrew Gilbert. 2020. Rethinking movie genre classification with fine-grained semantic clustering. arXiv preprint arXiv:2012.02639 (2020)."},{"key":"#cr-split#-e_1_3_2_1_5_1.1","doi-asserted-by":"crossref","unstructured":"Yuyang Gao Siyi Gu Junji Jiang Sungsoo\u00a0Ray Hong Dazhou Yu and Liang Zhao. 2022. Going Beyond XAI: A Systematic Survey for Explanation-Guided Learning. https:\/\/doi.org\/10.48550\/ARXIV.2212.03954 10.48550\/ARXIV.2212.03954","DOI":"10.3390\/math10193619"},{"key":"#cr-split#-e_1_3_2_1_5_1.2","unstructured":"Yuyang Gao Siyi Gu Junji Jiang Sungsoo\u00a0Ray Hong Dazhou Yu and Liang Zhao. 2022. Going Beyond XAI: A Systematic Survey for Explanation-Guided Learning. https:\/\/doi.org\/10.48550\/ARXIV.2212.03954"},{"key":"#cr-split#-e_1_3_2_1_6_1.1","doi-asserted-by":"crossref","unstructured":"M. Guarascio G. Manco and E. Ritacco. 2018. Deep learning. Encyclopedia of Bioinformatics and Computational Biology: ABC of Bioinformatics 1-3 (2018) 634-647. https:\/\/doi.org\/10.1016\/B978-0-12-809633-8.20352-X 10.1016\/B978-0-12-809633-8.20352-X","DOI":"10.1016\/B978-0-12-809633-8.20352-X"},{"key":"#cr-split#-e_1_3_2_1_6_1.2","doi-asserted-by":"crossref","unstructured":"M. Guarascio G. Manco and E. Ritacco. 2018. Deep learning. Encyclopedia of Bioinformatics and Computational Biology: ABC of Bioinformatics 1-3 (2018) 634-647. https:\/\/doi.org\/10.1016\/B978-0-12-809633-8.20352-X","DOI":"10.1016\/B978-0-12-809633-8.20352-X"},{"key":"e_1_3_2_1_7_1","first-page":"1929","article-title":"Dropout: A Simple Way to Prevent Neural Networks from Overfitting","volume":"15","author":"Hinton E.","year":"2014","unstructured":"G.\u00a0 E. Hinton , N. Srivastava , A. Krizhevsky , I. Sutskever , and R. Salakhutdinov . 2014 . Dropout: A Simple Way to Prevent Neural Networks from Overfitting . Journal of Machine Learning Research 15 (2014), 1929 \u2013 1958 . G.\u00a0E. Hinton, N. Srivastava, A. Krizhevsky, I. Sutskever, and R. Salakhutdinov. 2014. Dropout: A Simple Way to Prevent Neural Networks from Overfitting. Journal of Machine Learning Research 15 (2014), 1929\u20131958.","journal-title":"Journal of Machine Learning Research"},{"key":"e_1_3_2_1_8_1","volume-title":"Proc. of the 32Nd Int. Conf. on Machine Learning -","volume":"37","author":"Ioffe S.","unstructured":"S. Ioffe and C. Szegedy . 2015. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift . In Proc. of the 32Nd Int. Conf. on Machine Learning - Volume 37 (Lille, France) (ICML\u201915). 448\u2013456. S. Ioffe and C. Szegedy. 2015. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift. In Proc. of the 32Nd Int. Conf. on Machine Learning - Volume 37 (Lille, France) (ICML\u201915). 448\u2013456."},{"key":"e_1_3_2_1_9_1","first-page":"18","volume-title":"Proceedings of the 27th International Conference on Computational Linguistics. 2879\u20132891","author":"Kar Sudipta","year":"2018","unstructured":"Sudipta Kar , Suraj Maharjan , and Thamar Solorio . 2018 . Folksonomication: Predicting tags for movies from plot synopses using emotion flow encoded neural network . In Proceedings of the 27th International Conference on Computational Linguistics. 2879\u20132891 . https:\/\/aclanthology.org\/C 18 - 1244 Sudipta Kar, Suraj Maharjan, and Thamar Solorio. 2018. Folksonomication: Predicting tags for movies from plot synopses using emotion flow encoded neural network. In Proceedings of the 27th International Conference on Computational Linguistics. 2879\u20132891. https:\/\/aclanthology.org\/C18-1244"},{"key":"e_1_3_2_1_10_1","volume-title":"Deep Metric Learning: A Survey. Symmetry 11, 9","author":"M.","year":"2019","unstructured":"M. KAYA and H.\u00a0S. BILGE. 2019. Deep Metric Learning: A Survey. Symmetry 11, 9 ( 2019 ). https:\/\/doi.org\/10.3390\/sym11091066 10.3390\/sym11091066 M. KAYA and H.\u00a0S. BILGE. 2019. Deep Metric Learning: A Survey. Symmetry 11, 9 (2019). https:\/\/doi.org\/10.3390\/sym11091066"},{"key":"e_1_3_2_1_11_1","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2019.2963535"},{"key":"#cr-split#-e_1_3_2_1_12_1.1","doi-asserted-by":"crossref","unstructured":"Y. Le\u00a0Cun Y. Bengio and G. Hinton. 2015. Deep learning. Nature 521 7553 (2015) 436-444. https:\/\/doi.org\/10.1038\/nature14539 10.1038\/nature14539","DOI":"10.1038\/nature14539"},{"key":"#cr-split#-e_1_3_2_1_12_1.2","doi-asserted-by":"crossref","unstructured":"Y. Le\u00a0Cun Y. Bengio and G. Hinton. 2015. Deep learning. Nature 521 7553 (2015) 436-444. https:\/\/doi.org\/10.1038\/nature14539","DOI":"10.1038\/nature14539"},{"key":"e_1_3_2_1_13_1","volume-title":"Generating High-quality Movie Tags from Social Reviews: A Learning-driven Approach. In 2021 IEEE International Conferences on Internet of Things (iThings) and IEEE Green Computing & Communications","author":"Luo Zhenxiao","year":"2021","unstructured":"Zhenxiao Luo , Guopin Tang , Chen Wang , Yipeng Zhou , Xi Zheng , Jessie\u00a0Hui Wang , Gang Liu , and Di Wu. 2021. Generating High-quality Movie Tags from Social Reviews: A Learning-driven Approach. In 2021 IEEE International Conferences on Internet of Things (iThings) and IEEE Green Computing & Communications (GreenCom) and IEEE Cyber, Physical & Social Computing (CPSCom) and IEEE Smart Data (SmartData) and IEEE Congress on Cybermatics (Cybermatics) . 182\u2013189. https:\/\/doi.org\/10.1109\/iThings-GreenCom-CPSCom-SmartData-Cybermatics53846. 2021 .00040 10.1109\/iThings-GreenCom-CPSCom-SmartData-Cybermatics53846.2021.00040 Zhenxiao Luo, Guopin Tang, Chen Wang, Yipeng Zhou, Xi Zheng, Jessie\u00a0Hui Wang, Gang Liu, and Di Wu. 2021. Generating High-quality Movie Tags from Social Reviews: A Learning-driven Approach. In 2021 IEEE International Conferences on Internet of Things (iThings) and IEEE Green Computing & Communications (GreenCom) and IEEE Cyber, Physical & Social Computing (CPSCom) and IEEE Smart Data (SmartData) and IEEE Congress on Cybermatics (Cybermatics). 182\u2013189. https:\/\/doi.org\/10.1109\/iThings-GreenCom-CPSCom-SmartData-Cybermatics53846.2021.00040"},{"volume-title":"Foundations of Intelligent Systems","author":"Minici Marco","key":"e_1_3_2_1_14_1","unstructured":"Marco Minici , Francesco\u00a0Sergio Pisani , Massimo Guarascio , Erika De\u00a0Francesco , and Pasquale Lambardi . 2022. Learning and\u00a0Explanation of\u00a0Extreme Multi-label Deep Classification Models for\u00a0Media Content . In Foundations of Intelligent Systems . Springer International Publishing , Cham , 138\u2013148. https:\/\/doi.org\/10.1007\/978-3-031-16564-1_14 10.1007\/978-3-031-16564-1_14 Marco Minici, Francesco\u00a0Sergio Pisani, Massimo Guarascio, Erika De\u00a0Francesco, and Pasquale Lambardi. 2022. Learning and\u00a0Explanation of\u00a0Extreme Multi-label Deep Classification Models for\u00a0Media Content. In Foundations of Intelligent Systems. Springer International Publishing, Cham, 138\u2013148. https:\/\/doi.org\/10.1007\/978-3-031-16564-1_14"},{"key":"e_1_3_2_1_15_1","volume-title":"Proceedings of the 27th Int. Conf. on Machine Learning(ICML\u201910)","author":"Nair V.","year":"2010","unstructured":"V. Nair and G.\u00a0 E. Hinton . 2010 . Rectified Linear Units Improve Restricted Boltzmann Machines . In Proceedings of the 27th Int. Conf. on Machine Learning(ICML\u201910) . 807\u2013814. V. Nair and G.\u00a0E. Hinton. 2010. Rectified Linear Units Improve Restricted Boltzmann Machines. In Proceedings of the 27th Int. Conf. on Machine Learning(ICML\u201910). 807\u2013814."},{"key":"e_1_3_2_1_16_1","volume-title":"Article 180 (oct","author":"Ren Pengzhen","year":"2021","unstructured":"Pengzhen Ren , Yun Xiao , Xiaojun Chang , Po-Yao Huang , Zhihui Li , Brij\u00a0 B. Gupta , Xiaojiang Chen , and Xin Wang . 2021. A Survey of Deep Active Learning. ACM Comput. Surv. 54, 9 , Article 180 (oct 2021 ), 40\u00a0pages. https:\/\/doi.org\/10.1145\/3472291 10.1145\/3472291 Pengzhen Ren, Yun Xiao, Xiaojun Chang, Po-Yao Huang, Zhihui Li, Brij\u00a0B. Gupta, Xiaojiang Chen, and Xin Wang. 2021. A Survey of Deep Active Learning. ACM Comput. Surv. 54, 9, Article 180 (oct 2021), 40\u00a0pages. https:\/\/doi.org\/10.1145\/3472291"},{"key":"e_1_3_2_1_17_1","doi-asserted-by":"publisher","DOI":"10.1145\/2939672.2939778"},{"key":"e_1_3_2_1_18_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2015.7298682"},{"key":"e_1_3_2_1_19_1","first-page":"4","article-title":"A Systematic Analysis of Performance Measures for Classification","volume":"45","author":"Sokolova M.","year":"2009","unstructured":"M. Sokolova and G. Lapalme . 2009 . A Systematic Analysis of Performance Measures for Classification Tasks. Inf. Process. Manage. 45 , 4 (July 2009), 427\u2013437. https:\/\/doi.org\/10.1016\/j.ipm.2009.03.002 10.1016\/j.ipm.2009.03.002 M. Sokolova and G. Lapalme. 2009. A Systematic Analysis of Performance Measures for Classification Tasks. Inf. Process. Manage. 45, 4 (July 2009), 427\u2013437. https:\/\/doi.org\/10.1016\/j.ipm.2009.03.002","journal-title":"Tasks. Inf. Process. Manage."},{"key":"e_1_3_2_1_20_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.asoc.2017.08.029"},{"key":"e_1_3_2_1_21_1","doi-asserted-by":"publisher","DOI":"10.1007\/s11042-019-08513-0"}],"event":{"name":"IDEAS '23: International Database Engineered Applications Symposium Conference","acronym":"IDEAS '23","location":"Heraklion, Crete Greece"},"container-title":["International Database Engineered Applications Symposium Conference"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3589462.3589495","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3589462.3589495","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,19]],"date-time":"2025-06-19T00:03:16Z","timestamp":1750291396000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3589462.3589495"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,5,5]]},"references-count":24,"alternative-id":["10.1145\/3589462.3589495","10.1145\/3589462"],"URL":"https:\/\/doi.org\/10.1145\/3589462.3589495","relation":{},"subject":[],"published":{"date-parts":[[2023,5,5]]},"assertion":[{"value":"2023-05-26","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}