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Knowl. Discov. Data"],"published-print":{"date-parts":[[2025,5,31]]},"abstract":"<jats:p>\n            High-Dimensional and Incomplete (HDI) tensors contain a wealth of knowledge and patterns, which are typically utilized to characterize complex relationships between entities in a variety of industrial applications. Currently, the neural network-based tensor factorization model has shown superiority when handling the missing data in HDI tensors. However, it only uses the outer product of latent factors (LFs) of entities and neglects the interactions between the LF. In addition, the simple linear operation does not consider the nonlinear structure of the HDI tensor. To overcome the aforementioned issues, an Attention-mechanism-based Neural Latent-Factorization-of-Tensors (ANLFT) model is provided in this article. It encompasses three primary ideas: (a) incorporating the theory of neural networks with the latent factorization of tensor to construct the nonlinear structure in the HDI tensor effectively; (b) adopting the attention mechanism to depict the interactions between LF; (c) using the position-transitional particle swarm optimization backward propagation learning (\n            <jats:inline-formula content-type=\"math\/tex\">\n              <jats:tex-math notation=\"LaTeX\" version=\"MathJax\">\\(\\rm{P}^{2}\\)<\/jats:tex-math>\n            <\/jats:inline-formula>\n            BP) scheme to train the ANLFT model efficiently. The experimental results on eight HDI datasets show that the ANLFT model can obtain higher estimation performance gain than state-of-the-art models. The convergence performance of the proposed model is also competitive with that of state-of-the-art models.\n          <\/jats:p>","DOI":"10.1145\/3719295","type":"journal-article","created":{"date-parts":[[2025,2,25]],"date-time":"2025-02-25T09:50:22Z","timestamp":1740477022000},"page":"1-27","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":6,"title":["Attention-Mechanism-Based Neural Latent-Factorization-of-Tensors Model"],"prefix":"10.1145","volume":"19","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6639-5269","authenticated-orcid":false,"given":"Xiuqin","family":"Xu","sequence":"first","affiliation":[{"name":"School of Mathematics and Statistics, Fujian Normal University, Fuzhou, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2026-7178","authenticated-orcid":false,"given":"Mingwei","family":"Lin","sequence":"additional","affiliation":[{"name":"College of Computer and Cyber Security, Fujian Normal University, Fuzhou, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3547-2908","authenticated-orcid":false,"given":"Zeshui","family":"Xu","sequence":"additional","affiliation":[{"name":"School of Business, Sichuan University, Chengdu, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1348-5305","authenticated-orcid":false,"given":"Xin","family":"Luo","sequence":"additional","affiliation":[{"name":"Southwest University, Chongqing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2025,4,9]]},"reference":[{"key":"e_1_3_1_2_2","first-page":"210","volume-title":"Proceedings of the 2011 IEEE 22nd International Symposium on Software Reliability Engineering","author":"Zhang Yilei","year":"2011","unstructured":"Yilei Zhang, Zibin Zheng, and Michael R. Lyu. 2011. WSPred: A time-aware personalized QoS prediction framework for Web services. In Proceedings of the 2011 IEEE 22nd International Symposium on Software Reliability Engineering, 210\u2013219."},{"key":"e_1_3_1_3_2","first-page":"1","article-title":"FCT: a fully-distributed context-aware trust model for location based service recommendation","volume":"60","author":"Liu Zhiquan","year":"2017","unstructured":"Liu, Zhiquan, Jianfeng Ma. 2017. FCT: a fully-distributed context-aware trust model for location based service recommendation. Science China Information Sciences 60 (2017), 1\u201316.","journal-title":"Science China Information Sciences"},{"issue":"10","key":"e_1_3_1_4_2","doi-asserted-by":"crossref","first-page":"11001","DOI":"10.1109\/TITS.2023.3279321","article-title":"HRST-LR: A Hessian regularization spatio-temporal low rank algorithm for traffic data imputation","volume":"24","author":"Xu Xiuqin","year":"2023","unstructured":"Xiuqin Xu, Mingwei Lin, Xin Luo, and Zeshiu Xu. 2023. HRST-LR: A Hessian regularization spatio-temporal low rank algorithm for traffic data imputation. IEEE Transactions on Intelligent Transportation Systems 24, 10 (2023), 11001\u201311017.","journal-title":"IEEE Transactions on Intelligent Transportation Systems"},{"issue":"9","key":"e_1_3_1_5_2","doi-asserted-by":"crossref","first-page":"3473","DOI":"10.1109\/TFUZZ.2021.3117442","article-title":"A fast fuzzy clustering algorithm for complex networks via a generalized momentum method","volume":"30","author":"Hu Lun","year":"2022","unstructured":"Lun Hu, Xiangyu Pan, Zehai Tang, and Xin Luo. 2022. A fast fuzzy clustering algorithm for complex networks via a generalized momentum method. IEEE Transactions on Fuzzy Systems 30, 9 (2022), 3473\u20133485.","journal-title":"IEEE Transactions on Fuzzy Systems"},{"key":"e_1_3_1_6_2","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3584862","article-title":"A generalized deep learning clustering algorithm based on non-negative matrix factorization","volume":"17","author":"Wang Dexian","year":"2023","unstructured":"Dexian Wang, Tianrui Li, Ping Deng, Fan Zhang, Wei Huang, Pengfei Zhang, and Jia Liu. 2023. A generalized deep learning clustering algorithm based on non-negative matrix factorization. ACM Transactions on Knowledge Discovery from Data 17, 7 (2023), 1\u201320.","journal-title":"ACM Transactions on Knowledge Discovery from Data"},{"issue":"6","key":"e_1_3_1_7_2","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3578520","article-title":"Nonnegative matrix factorization based on node centrality for community detection","volume":"17","author":"Su Sixing","year":"2023","unstructured":"Sixing Su, Jiewen Guan, Bilian Chen, and Xin Huang. 2023. Nonnegative matrix factorization based on node centrality for community detection. ACM Transactions on Knowledge Discovery from Data 17, 6 (2023), 1\u201321.","journal-title":"ACM Transactions on Knowledge Discovery from Data"},{"issue":"8","key":"e_1_3_1_8_2","doi-asserted-by":"crossref","first-page":"13275","DOI":"10.1109\/TITS.2021.3123276","article-title":"A novel prediction-based temporal graph routing algorithm for software-defined vehicular networks","volume":"23","author":"Zhao Liang","year":"2021","unstructured":"Liang Zhao, Zhuhui Li, Ahmed Y. Al-Dubai, Geyong Min, Jiajia Li, Ammar Hawbani, and Albert Y. Zomaya. 2021. A novel prediction-based temporal graph routing algorithm for software-defined vehicular networks. IEEE Transactions on Intelligent Transportation Systems 23, 8 (2021), 13275\u201313290.","journal-title":"IEEE Transactions on Intelligent Transportation Systems"},{"issue":"4","key":"e_1_3_1_9_2","doi-asserted-by":"crossref","first-page":"925","DOI":"10.1109\/TPAMI.2019.2891760","article-title":"Tensor robust principal component analysis with a new tensor nuclear norm","volume":"42","author":"Lu Caiyi","year":"2020","unstructured":"Caiyi Lu, Jiashi Feng, Yudong Liu, Wei Liu, Zhouchen Lin, and Shuicheng Yan. 2020. Tensor robust principal component analysis with a new tensor nuclear norm. 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In Proceedings of the 2023 IEEE International Conference on Big Data (BigData \u201823), 86\u201395."},{"issue":"8","key":"e_1_3_1_13_2","first-page":"3958","article-title":"Position-transitional particle Swarm optimization-incorporated latent factor analysis","volume":"38","author":"Luo Xin","year":"2022","unstructured":"Xin Luo, Ye Yuan, Suli Chen, Nianyin Zeng, and Zidong Wang. 2022. Position-transitional particle Swarm optimization-incorporated latent factor analysis. IEEE Transactions on Knowledge and Data Engineering 38, 8 (2022), 3958\u20133970.","journal-title":"IEEE Transactions on Knowledge and Data Engineering"},{"issue":"3","key":"e_1_3_1_14_2","doi-asserted-by":"crossref","first-page":"455","DOI":"10.1137\/07070111X","article-title":"Tensor decompositions and applications","volume":"51","author":"Kolda Tamara G.","year":"2009","unstructured":"Tamara G. Kolda and Brett W. Bader. 2009. Tensor decompositions and applications. SIAM Review 51, 3 (2009), 455\u2013500.","journal-title":"SIAM Review"},{"issue":"2","key":"e_1_3_1_15_2","first-page":"1","article-title":"Tensors for data mining and data fusion: Models, applications, and scalable algorithms","volume":"8","author":"Papalexakis Evangelos E.","year":"2016","unstructured":"Evangelos E. Papalexakis, Christos Faloutsos, and Nicholas D. Sidiropoulos. 2016. Tensors for data mining and data fusion: Models, applications, and scalable algorithms. ACM Transactions on Intelligent Systems and Technology (TIST) 8, 2 (2016), 1\u201344.","journal-title":"ACM Transactions on Intelligent Systems and Technology (TIST"},{"issue":"8","key":"e_1_3_1_16_2","first-page":"4355","article-title":"Robust low-tubal-rank tensor recovery from binary measurements","volume":"44","author":"Hou Jingyao","year":"2022","unstructured":"Jingyao Hou, Feng Zhang, Haiquan Wang, Jianjun Wang, Yao Wang, and Deyu Meng. 2022. Robust low-tubal-rank tensor recovery from binary measurements. 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Neurocomputing 367, 20 (2019), 299\u2013307.","journal-title":"Neurocomputing"},{"key":"e_1_3_1_18_2","first-page":"639","volume-title":"Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval","author":"He Xiangnan","year":"2020","unstructured":"Xiangnan He, Kuan Deng, Xiang Wang, Yan Li, YongDong Zhang, and Meng Wang. 2020. LightGCN: Simplifying and powering graph convolution network for recommendation. In Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval, 639\u2013648."},{"issue":"6","key":"e_1_3_1_19_2","doi-asserted-by":"crossref","first-page":"3359","DOI":"10.1109\/TITS.2020.2984175","article-title":"Embedding traffic network characteristics using tensor for improved traffic prediction","volume":"22","author":"Bhanu Manish","year":"2021","unstructured":"Manish Bhanu, Jo\u00e3o Mendes-Moreira, and Joydeep Chandra. 2021. Embedding traffic network characteristics using tensor for improved traffic prediction. IEEE Transactions on Intelligent Transportation Systems 22, 6 (2021), 3359\u20133371.","journal-title":"IEEE Transactions on Intelligent Transportation Systems"},{"issue":"3","key":"e_1_3_1_20_2","first-page":"909","article-title":"Coupled graphs and tensor factorization for recommender systems and community detection","volume":"33","author":"Ioannidis Vassilis N.","year":"2021","unstructured":"Vassilis N. Ioannidis, Ahmed S. Zamzam, Georgios B. Giannakis, and Nicholas D. Sidiropoulos. 2021. Coupled graphs and tensor factorization for recommender systems and community detection. IEEE Transactions on Knowledge and Data Engineering 33, 3 (2021), 909\u2013920.","journal-title":"IEEE Transactions on Knowledge and Data Engineering"},{"issue":"2","key":"e_1_3_1_21_2","doi-asserted-by":"crossref","first-page":"1519","DOI":"10.1109\/TSG.2020.3030566","article-title":"A regularized tensor completion approach for PMU data recovery","volume":"12","author":"Ghasemkhani Amir","year":"2021","unstructured":"Amir Ghasemkhani, Iman Niazazari, Yunchuan Liu, Hanif Livani, Virgilio A. Centeno, and Lei Yang. 2021. A regularized tensor completion approach for PMU data recovery. IEEE Transactions on Smart Grid 12, 2 (2021), 1519\u20131528.","journal-title":"IEEE Transactions on Smart Grid"},{"issue":"6","key":"e_1_3_1_22_2","doi-asserted-by":"crossref","first-page":"1803","DOI":"10.1109\/TNNLS.2018.2873655","article-title":"Feature extraction for incomplete data via low-rank tensor decomposition with feature regularization","volume":"30","author":"Shi Qiquan","year":"2019","unstructured":"Qiquan Shi, Yiu-Ming Cheung, Qibin Zhao, and Haiping Lu. 2019. Feature extraction for incomplete data via low-rank tensor decomposition with feature regularization. IEEE Transactions on Neural Networks and Learning Systems, 30, 6 (2019), 1803\u20131817.","journal-title":"IEEE Transactions on Neural Networks and Learning Systems"},{"key":"e_1_3_1_23_2","doi-asserted-by":"crossref","first-page":"585","DOI":"10.1145\/2566486.2568001","volume-title":"Proceedings of the 23rd International Conference on World Wide Web","author":"Zhang Wancai","year":"2014","unstructured":"Wancai Zhang, Hailong Sun, Xudong Liu, and Xiaohui Guo. 2014. Temporal QoS aware Web service recommendation via non-negative tensor factorization. In Proceedings of the 23rd International Conference on World Wide Web, 585\u2013596."},{"issue":"5","key":"e_1_3_1_24_2","doi-asserted-by":"crossref","first-page":"1798","DOI":"10.1109\/TCYB.2019.2903736","article-title":"Temporal pattern-aware QoS prediction via biased non-negative latent factorization of tensors","volume":"50","author":"Luo Xin","year":"2020","unstructured":"Xin Luo, Hao Wu, Huaqiang Yuan, and Mengchu Zhou. 2020. Temporal pattern-aware QoS prediction via biased non-negative latent factorization of tensors. IEEE Transactions on Cybernetics 50, 5 (2020), 1798\u20131809.","journal-title":"IEEE Transactions on Cybernetics"},{"issue":"1","key":"e_1_3_1_25_2","doi-asserted-by":"crossref","first-page":"47","DOI":"10.1109\/TSC.2016.2584058","article-title":"Multi-dimensional QoS prediction for service recommendations","volume":"12","author":"Wang Shangguang","year":"2019","unstructured":"Shangguang Wang, You Ma, Bo Cheng, Fangchun Yang, and Rong N. Chang. 2019. Multi-dimensional QoS prediction for service recommendations. IEEE Transactions on Services Computing 12, 1 (2019), 47\u201357.","journal-title":"IEEE Transactions on Services Computing"},{"issue":"3","key":"e_1_3_1_26_2","first-page":"1","article-title":"History-enhanced and uncertainty-aware trajectory recovery via attentive neural network","volume":"18","author":"Xia Tong","year":"2023","unstructured":"Tong Xia, Yong Li, Yunhan Qi, Jie Feng, Fengli Xu, Funing Sun, Diansheng Guo, and Depeng Jin. 2023. History-enhanced and uncertainty-aware trajectory recovery via attentive neural network. ACM Transactions on Knowledge Discovery from Data 18, 3 (2023), 1\u201322.","journal-title":"ACM Transactions on Knowledge Discovery from Data"},{"issue":"1","key":"e_1_3_1_27_2","doi-asserted-by":"crossref","first-page":"278","DOI":"10.1109\/JAS.2018.7511255","article-title":"Neural network based adaptive tracking control for a class of pure feedback nonlinear systems with input saturation","volume":"6","author":"Zerari Nassira","year":"2019","unstructured":"Nassira Zerari, Mohamed Chemachema, and Najib Essounbouli. 2019. Neural network based adaptive tracking control for a class of pure feedback nonlinear systems with input saturation. IEEE\/CAA Journal of Automatica Sinica 6, 1 (2019), 278\u2013290.","journal-title":"IEEE\/CAA Journal of Automatica Sinica"},{"issue":"1","key":"e_1_3_1_28_2","doi-asserted-by":"crossref","first-page":"138","DOI":"10.1109\/TNNLS.2018.2830119","article-title":"Enhanced robot speech recognition using biomimetic binaural sound source localization","volume":"30","author":"D\u00e1vila-Chac\u00f3n Jorge","year":"2019","unstructured":"Jorge D\u00e1vila-Chac\u00f3n, Jindong Liu, and Stefan Wermter. 2019. Enhanced robot speech recognition using biomimetic binaural sound source localization. IEEE Transactions on Neural Networks and Learning Systems 30, 1 (2019), 138\u2013150.","journal-title":"IEEE Transactions on Neural Networks and Learning Systems"},{"issue":"6","key":"e_1_3_1_29_2","doi-asserted-by":"crossref","first-page":"1428","DOI":"10.1109\/JAS.2019.1911441","article-title":"Single image rain removal using image decomposition and a dense network","volume":"6","author":"Lian Qiusheng","year":"2019","unstructured":"Qiusheng Lian, Wenfeng Yan, Xiaohua Zhang, and Shuzhen Chen. 2019. Single image rain removal using image decomposition and a dense network. IEEE\/CAA Journal of Automatica Sinica, 6 6 (2019), 1428\u20131437.","journal-title":"IEEE\/CAA Journal of Automatica Sinica"},{"issue":"5","key":"e_1_3_1_30_2","doi-asserted-by":"crossref","first-page":"1998","DOI":"10.1109\/TNNLS.2017.2690379","article-title":"Tensor-factorized neural networks","volume":"29","author":"Chien Jen-Tzung","year":"2018","unstructured":"Jen-Tzung Chien and Yi-Ting Bao. 2018. Tensor-factorized neural networks. IEEE Transactions on Neural Networks and Learning Systems 29, 5 (2018), 1998\u20132011.","journal-title":"IEEE Transactions on Neural Networks and Learning Systems"},{"issue":"1","key":"e_1_3_1_31_2","doi-asserted-by":"crossref","first-page":"780","DOI":"10.1109\/TNSM.2021.3056912","article-title":"Tensor-based recurrent neural network and multi-modal prediction with its applications in traffic network management","volume":"18","author":"Wu Qing","year":"2021","unstructured":"Qing Wu, Zhe Jiang, Kewei Hong, Huazhong Liu, Laurence T. Yang, and Jihong Ding. 2021. Tensor-based recurrent neural network and multi-modal prediction with its applications in traffic network management. IEEE Transactions on Network and Service Management 18, 1 (2021), 780\u2013792.","journal-title":"IEEE Transactions on Network and Service Management"},{"issue":"2","key":"e_1_3_1_32_2","doi-asserted-by":"crossref","first-page":"318","DOI":"10.1109\/TPAMI.2017.2677439","article-title":"Deep unfolding for topic models","volume":"29","author":"Chien Jen-Tzung","year":"2018","unstructured":"Jen-Tzung Chien and Chao-Hsi Lee. 2018. Deep unfolding for topic models. IEEE Transactions on Pattern Analysis and Machine Intelligence 29, 2 (2018), 318\u2013331.","journal-title":"IEEE Transactions on Pattern Analysis and Machine Intelligence"},{"issue":"2","key":"e_1_3_1_33_2","doi-asserted-by":"crossref","first-page":"601","DOI":"10.1109\/TNNLS.2018.2846646","article-title":"Dendritic neuron model with effective learning algorithms for classification, approximation and prediction","volume":"30","author":"Gao Shangce","year":"2019","unstructured":"Shangce Gao, Mengchu Zhou, Yirui Wang, Jiujun Cheng, Hanaki Yachi, and Jiahai Wang. 2019. Dendritic neuron model with effective learning algorithms for classification, approximation and prediction. IEEE Transactions on Neural Networks and Learning Systems 30, 2 (2019), 601\u2013614.","journal-title":"IEEE Transactions on Neural Networks and Learning Systems"},{"issue":"6","key":"e_1_3_1_34_2","first-page":"6148","article-title":"Neulft: A Novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors","volume":"35","author":"Luo Xin","year":"2023","unstructured":"Xin Luo, Hao Wu, and Zechao Li. 2023. Neulft: A Novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors. IEEE Transactions on Knowledge and Data Engineering 35, 6 (2023), 6148\u20136166.","journal-title":"IEEE Transactions on Knowledge and Data Engineering"},{"issue":"8","key":"e_1_3_1_35_2","doi-asserted-by":"crossref","first-page":"3751","DOI":"10.1109\/TKDE.2024.3363703","article-title":"Differentiable clustering for graph attention","volume":"36","author":"Zhou Haicang","year":"2024","unstructured":"Haicang Zhou, Tiantian He, Yew-Soon Ong, Gao Cong and Quan Chen. 2024. Differentiable clustering for graph attention. IEEE Transactions on Knowledge and Data Engineering 36, 8 (2024), 3751-3764.","journal-title":"IEEE Transactions on Knowledge and Data Engineering"},{"key":"e_1_3_1_36_2","doi-asserted-by":"crossref","first-page":"104129","DOI":"10.1016\/j.artint.2024.104129","article-title":"Polarized message-passing in graph neural networks","volume":"331","author":"He Tiantian","year":"2024","unstructured":"Tiantian He, Yang Liu, Yew-Soon Ong, Xiaohu Wu, and Xin Luo. 2024. Polarized message-passing in graph neural networks. Artificial Intelligence 331 (2024), 104129.","journal-title":"Artificial Intelligence"},{"key":"e_1_3_1_37_2","unstructured":"Tiantian He Yew-Soon Ong and Lu Bai. 2021. Learning conjoint attentions for graph neural nets. arXiv:2102.03147. Retrieved from https:\/\/arxiv.org\/abs\/2102.03147"},{"issue":"8","key":"e_1_3_1_38_2","doi-asserted-by":"crossref","first-page":"7467","DOI":"10.1109\/TCSVT.2024.3370668","article-title":"Evit: Privacy-preserving image retrieval via encrypted vision transformer in cloud computing","volume":"34","author":"Feng Qihua","year":"2024","unstructured":"Qihua Feng, Peiya Li, Zhixun Lu, Chaozhuo Li, Zefan Wang, and Zhiquan Liu. 2024. Evit: Privacy-preserving image retrieval via encrypted vision transformer in cloud computing. IEEE Transactions on Circuits and Systems for Video Technology 34, 8 (2024), 7467\u20137483.","journal-title":"IEEE Transactions on Circuits and Systems for Video Technology"},{"issue":"11","key":"e_1_3_1_39_2","doi-asserted-by":"crossref","first-page":"4461","DOI":"10.1109\/TNNLS.2019.2955567","article-title":"An efficient group recommendation model with multiattention-based neural networks","volume":"31","author":"Huang Zhenhua","year":"2020","unstructured":"Zhenhua Huang, Xin Xu, Honghao Zhu, and Mengchu Zhou. 2020. An efficient group recommendation model with multiattention-based neural networks. IEEE Transactions on Neural Networks and Learning Systems 31, 11 (2020), 4461\u20134474.","journal-title":"IEEE Transactions on Neural Networks and Learning Systems"},{"issue":"3","key":"e_1_3_1_40_2","doi-asserted-by":"crossref","first-page":"1004","DOI":"10.1109\/TCSS.2022.3158318","article-title":"Time-aware attention-based gated network for credit card fraud detection by extracting transactional behaviors","volume":"10","author":"Xie Yu","year":"2023","unstructured":"Yu Xie, Guanjun Liu, Chungang Yan, Changjun Jiang, and Mengchu Zhou. 2023. Time-aware attention-based gated network for credit card fraud detection by extracting transactional behaviors. IEEE Transactions on Computational Social Systems 10, 3 (2023), 1004\u20131016.","journal-title":"IEEE Transactions on Computational Social Systems"},{"issue":"6","key":"e_1_3_1_41_2","first-page":"1536","article-title":"A local-global attention fusion framework with tensor decomposition for medical diagnosis","volume":"11","author":"Wu Peishu","year":"2023","unstructured":"Peishu Wu, Han Li, Liwei Hu, Jirong Ge, and Nianyin Zeng. 2023. A local-global attention fusion framework with tensor decomposition for medical diagnosis. IEEE\/CAA Journal of Automatica Sinica, 11 6 (2023), 1536\u20131538, 2024.","journal-title":"IEEE\/CAA Journal of Automatica Sinica"},{"key":"e_1_3_1_42_2","first-page":"189","volume-title":"Proceedings of the 2021 IEEE International Conference on Bioinformatics and Biomedicine (BIBM \u201821)","author":"Luo Jiawei","year":"2021","unstructured":"Jiawei Luo, Zihan Lai, Cong Shen, Pei Liu, and Heyuan Shi. 2021. 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Science 367, 6437 (2020), 83\u201387.","journal-title":"Science"},{"key":"e_1_3_1_47_2","first-page":"197","volume-title":"Proceedings of the 2023 IEEE 10th International Conference on Cyber Security and Cloud Computing (CSCloud \u201823)","author":"Tang Wenyu","year":"2023","unstructured":"Wenyu Tang, Mingdong Tang, and Fenfang Xie. 2023. Temporal-aware QoS prediction based on tensor factorization and self-attention for cloud services. In Proceedings of the 2023 IEEE 10th International Conference on Cyber Security and Cloud Computing (CSCloud \u201823), 197\u2013202."},{"issue":"11","key":"e_1_3_1_48_2","doi-asserted-by":"crossref","first-page":"13276","DOI":"10.1109\/TITS.2023.3279412","article-title":"Accurately predicting quality of services in IoT via using self-attention representation and deep factorization machines","volume":"24","author":"Tang Mingdong","year":"2023","unstructured":"Mingdong Tang, Wenyu Tang, and Fenfang Xie. 2023. Accurately predicting quality of services in IoT via using self-attention representation and deep factorization machines. IEEE Transactions on Intelligent Transportation Systems 24, 11 (2023), 13276\u201313285.","journal-title":"IEEE Transactions on Intelligent Transportation Systems"},{"issue":"2","key":"e_1_3_1_49_2","first-page":"916","article-title":"Latent factor-based recommenders relying on extended stochastic gradient descent algorithms","volume":"51","author":"Luo Xin","year":"2019","unstructured":"Xin Luo, Dexian Wang, MengChu Zhou, and Huaqiang Yuan. 2019. Latent factor-based recommenders relying on extended stochastic gradient descent algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51, 2 (2019), 916\u2013926.","journal-title":"IEEE Transactions on Systems, Man, and Cybernetics: Systems"},{"issue":"1","key":"e_1_3_1_50_2","doi-asserted-by":"crossref","first-page":"145","DOI":"10.1016\/S0893-6080(98)00116-6","article-title":"On the momentum term in gradient descent learning algorithms","volume":"12","author":"Qian Ning","year":"1999","unstructured":"Ning Qian. 1999. On the momentum term in gradient descent learning algorithms. Neural Networks 12, 1 (1999), 145\u2013151.","journal-title":"Neural Networks"},{"issue":"7","key":"e_1_3_1_51_2","first-page":"2121","article-title":"Adaptive subgradient methods for online learning and stochastic optimization","volume":"12","author":"Duchi John","year":"2011","unstructured":"John Duchi, Elad Hazan, and Yoram Singer. 2011. Adaptive subgradient methods for online learning and stochastic optimization. Journal of Machine Learning Research 12, 7 (2011), 2121\u20132159.","journal-title":"Journal of Machine Learning Research"},{"key":"e_1_3_1_52_2","unstructured":"Matthew D. Zeiler. 2012. ADADELTA: An adaptive learning rate method. arXiv:1212.5701. Retrieved from https:\/\/arxiv.org\/abs\/1212.5701"},{"key":"e_1_3_1_53_2","first-page":"1","volume-title":"Proceedings of the 3rd International Conference on Learning Representations (ICLR \u201815)","author":"Kingma Diederik P.","year":"2015","unstructured":"Diederik P. Kingma and Jimmy Ba. 2015. Adam: A method for stochastic optimization. In Proceedings of the 3rd International Conference on Learning Representations (ICLR \u201815), 1\u201315."},{"key":"e_1_3_1_54_2","first-page":"363","volume-title":"Proceedings of the 2017 4th International Conference on Industrial Engineering and Applications (ICIEA \u201817)","author":"Andayani Ulfi","year":"2017","unstructured":"Ulfi Andayani, Ema Budhiarti Nababan, Baihaqi Siregar, Muhammad Anggia Muchtar, Tigor Hamonangan Nasution, and Ikhsan Siregar. 2017. Optimization backpropagation algorithm based on Nguyen-Widrom adaptive weight and adaptive learning rate. In Proceedings of the 2017 4th International Conference on Industrial Engineering and Applications (ICIEA \u201817), 363\u2013367."},{"issue":"6","key":"e_1_3_1_55_2","doi-asserted-by":"crossref","first-page":"1247","DOI":"10.1109\/TNNLS.2014.2335749","article-title":"Learning from adaptive neural dynamic surface control of strict-feedback systems","volume":"26","author":"Wang Min","year":"2015","unstructured":"Min Wang and Cong Wang. 2015. Learning from adaptive neural dynamic surface control of strict-feedback systems. IEEE Transactions on Neural Networks and Learning Systems 26, 6 (2015), 1247\u20131259.","journal-title":"IEEE Transactions on Neural Networks and Learning Systems"},{"key":"e_1_3_1_56_2","doi-asserted-by":"crossref","first-page":"447","DOI":"10.1145\/1557019.1557072","volume-title":"Proceedings of the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining","author":"Koren Yehuda","year":"2009","unstructured":"Yehuda Koren. 2009. Collaborative filtering with temporal dynamics. In Proceedings of the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 447\u2013456."},{"key":"e_1_3_1_57_2","doi-asserted-by":"crossref","first-page":"81","DOI":"10.1145\/1718487.1718498","volume-title":"Proceedings of the 3rd ACM International Conference on Web Search and Data Mining February","volume":"2010","author":"Rendle Steffen","year":"2010","unstructured":"Steffen Rendle and Lars Schmidt-Thieme. 2010. Pairwise interaction tensor factorization for personalized tag recommendation. In Proceedings of the 3rd ACM International Conference on Web Search and Data Mining February 2010, 81\u201390."},{"issue":"3","key":"e_1_3_1_58_2","doi-asserted-by":"crossref","first-page":"3843","DOI":"10.1109\/TNSM.2021.3074547","article-title":"A tensor-based approach for the QoS evaluation in service-oriented environments","volume":"18","author":"Su Xing","year":"2021","unstructured":"Xing Su, Minjie Zhang, Yi Liang, Zhi Cai, Limin Guo, and Zhiming Ding. 2021. A tensor-based approach for the QoS evaluation in service-oriented environments. IEEE Transactions on Network and Service Management 18, 3 (2021), 3843\u20133857.","journal-title":"IEEE Transactions on Network and Service Management"},{"key":"e_1_3_1_59_2","first-page":"1","article-title":"QoS prediction for Web services in cloud environments based on swarm intelligence search","volume":"259","author":"Chen Jifu","year":"2023","unstructured":"Jifu Chen, Chengying Mao, and William Wei Song. 2023. QoS prediction for Web services in cloud environments based on swarm intelligence search. Knowledge-Based Systems 259 (2023), 1\u201316.","journal-title":"Knowledge-Based Systems"},{"key":"e_1_3_1_60_2","first-page":"1","volume-title":"Proceedings of 3rd Workshop on Deep Learning Practice for High-Dimensional Sparse Data with KDD","author":"Guo Wei","year":"2021","unstructured":"Wei Guo, Yang Yang, Yaochen Hu, Chuyuan Wang, Huifeng Guo, Yingxue Zhang, Ruiming Tang, Weinan Zhang, and Xiuqiang He. 2021. Deep graph convolutional networks with hybrid normalization for accurate and diverse recommendation. In Proceedings of 3rd Workshop on Deep Learning Practice for High-Dimensional Sparse Data with KDD, 1\u20139."},{"issue":"1","key":"e_1_3_1_61_2","first-page":"1","article-title":"Statistical comparisons of classifiers over multiple data sets","volume":"7","author":"Dem\u0161ar Janez","year":"2006","unstructured":"Janez Dem\u0161ar. 2006. Statistical comparisons of classifiers over multiple data sets. The Journal of Machine Learning Research 7, 1 (2006), 1\u201330.","journal-title":"The Journal of Machine Learning Research"},{"issue":"10","key":"e_1_3_1_62_2","doi-asserted-by":"crossref","first-page":"2039","DOI":"10.1109\/JAS.2024.124806","article-title":"Evolution and role of optimizers in training deep learning models","volume":"11","author":"Wen XiaoHao","year":"2024","unstructured":"XiaoHao Wen and MengChu Zhou. 2024. Evolution and role of optimizers in training deep learning models. IEEE\/CAA Journal of Automatica Sinica 11, 10 (2024), 2039\u20132042.","journal-title":"IEEE\/CAA Journal of Automatica Sinica"},{"issue":"5","key":"e_1_3_1_63_2","doi-asserted-by":"crossref","first-page":"178","DOI":"10.1109\/MNET.011.1900587","article-title":"Intelligent digital twin-based software-defined vehicular networks","volume":"34","author":"Zhao Liang","year":"2020","unstructured":"Liang Zhao, Guangjie Han, Zhuhui Li, and Lei Shu. 2020. Intelligent digital twin-based software-defined vehicular networks. IEEE Network 34, 5 (2020), 178\u2013184.","journal-title":"IEEE Network"},{"issue":"10","key":"e_1_3_1_64_2","doi-asserted-by":"crossref","first-page":"15065","DOI":"10.1109\/TITS.2024.3398602","article-title":"Overtaking feasibility prediction for mixed connected and connectionless vehicles","volume":"25","author":"Zhao Liang","year":"2024","unstructured":"Liang Zhao, Hui Qian, Ammar Hawbani, Ahmed Y. Al-Dubai, Zhiyuan Tan, Keping Yu, and Albert Zomaya. 2024. Overtaking feasibility prediction for mixed connected and connectionless vehicles. 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