{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,19]],"date-time":"2026-03-19T14:01:13Z","timestamp":1773928873445,"version":"3.50.1"},"reference-count":48,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2026,3,18]],"date-time":"2026-03-18T00:00:00Z","timestamp":1773792000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"},{"start":{"date-parts":[[2026,3,19]],"date-time":"2026-03-19T00:00:00Z","timestamp":1773878400000},"content-version":"vor","delay-in-days":1,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["J Cloud Comp"],"DOI":"10.1186\/s13677-026-00889-3","type":"journal-article","created":{"date-parts":[[2026,3,18]],"date-time":"2026-03-18T12:06:08Z","timestamp":1773835568000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["CloudSeqAnim: cloud-based sequential behavior modeling for large-scale animation content services"],"prefix":"10.1186","volume":"15","author":[{"given":"Chao","family":"Guo","sequence":"first","affiliation":[]},{"given":"Mohammad Mehdi","family":"Ghasemi","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2026,3,18]]},"reference":[{"key":"889_CR1","unstructured":"Hidasi B, Karatzoglou A, Baltrunas L, Tikk D (2015) Session-based recommendations with recurrent neural networks. arXiv preprint arXiv:1511.06939"},{"key":"889_CR2","doi-asserted-by":"crossref","unstructured":"Kang WC, McAuley J (2018 November) Self-attentive sequential recommendation. In: 2018 IEEE international conference on data mining (ICDM), IEEE, pp 197\u2013206","DOI":"10.1109\/ICDM.2018.00035"},{"key":"889_CR3","doi-asserted-by":"crossref","unstructured":"Liu Q, Zeng Y, Mokhosi R et al (2018) STAMP: short-term attention\/memory priority model for session-based recommendation[C]. In: Proceedings of the 24th ACM SIGKDD international conference on knowledge discovery & data mining, pp 1831\u20131839","DOI":"10.1145\/3219819.3219950"},{"key":"889_CR4","doi-asserted-by":"crossref","unstructured":"Rendle S, Freudenthaler C, Schmidt-Thieme L (2010) Factorizing personalized markov chains for next-basket recommendation[C]. In: Proceedings of the 19th international conference on World Wide Web, pp 811\u2013820","DOI":"10.1145\/1772690.1772773"},{"key":"889_CR5","doi-asserted-by":"publisher","first-page":"119048","DOI":"10.1016\/j.eswa.2022.119048","volume":"213","author":"D Canturk","year":"2023","unstructured":"Canturk D, Karagoz P, Kim SW et al (2023) Trust-aware location recommendation in location-based social networks: a graph-based approach[J]. Expert Syst Appl 213:119048","journal-title":"Expert Syst Appl"},{"key":"889_CR6","doi-asserted-by":"publisher","first-page":"90","DOI":"10.1016\/j.is.2017.07.007","volume":"71","author":"V Gkatziaki","year":"2017","unstructured":"Gkatziaki V, Giatsoglou M, Chatzakou D et al (2017) DynamiCITY: revealing city dynamics from citizens social media broadcasts[J]. Inform Syst 71:90\u2013102","journal-title":"Inform Syst"},{"key":"889_CR7","unstructured":"Peter M, Timothy G (2011) The NIST definition of cloud computing: recommendations of the National Institute of Standards and Technology[J]. In: National Institute of Standards and Technology (NIST) Special Publication, pp 800\u2013145"},{"issue":"6","key":"889_CR8","doi-asserted-by":"publisher","first-page":"176349","DOI":"10.1007\/s11704-023-2689-5","volume":"17","author":"M Wen","year":"2023","unstructured":"Wen M, Lin R, Wang H et al (2023) Large sequence models for sequential decision-making: a survey[J]. Front Comput Sci 17(6):176349","journal-title":"Front Comput Sci"},{"key":"889_CR9","unstructured":"Gu A, Dao T (2024) Mamba: linear-time sequence modeling with selective state spaces[C]. In: First conference on language modeling"},{"issue":"1","key":"889_CR10","doi-asserted-by":"publisher","first-page":"70","DOI":"10.1145\/2898442.2898444","volume":"14","author":"B Burns","year":"2016","unstructured":"Burns B, Grant B, Oppenheimer D et al (2016) Borg, Omega, and Kubernetes: lessons learned from three container-management systems over a decade[J]. Queue 14(1):70\u201393","journal-title":"Queue"},{"issue":"3","key":"889_CR11","doi-asserted-by":"publisher","first-page":"2003606","DOI":"10.1007\/s11704-025-41329-w","volume":"20","author":"PAN L W","year":"2026","unstructured":"Pan LW, Pan W , Wei MY et al (2026) A survey on sequential recommendation[J]. Front Comput Sci 20(3):2003606","journal-title":"Front Comput Sci"},{"key":"889_CR12","doi-asserted-by":"publisher","first-page":"102427","DOI":"10.1016\/j.is.2024.102427","volume":"125","author":"TF BOKA","year":"2024","unstructured":"Boka TF, Niu Z, Neupane RB (2024) A survey of sequential recommendation systems: techniques, evaluation, and future directions[J]. Inform Syst 125:102427","journal-title":"Inform Syst"},{"key":"889_CR13","doi-asserted-by":"publisher","DOI":"10.26599\/TST.2024.9010193","author":"S Xu","year":"2025","unstructured":"Xu S, Xiang Q, Fan Y, Zhang J (2025) Improving sequential service recommendation via a novel neighborhood-augmented graph collaborative attention network[J]. Tsinghua Sci Technol. https:\/\/doi.org\/10.26599\/TST.2024.9010193","journal-title":"Tsinghua Sci Technol"},{"issue":"2","key":"889_CR14","doi-asserted-by":"publisher","first-page":"273","DOI":"10.26599\/BDMA.2024.9020060","volume":"8","author":"Z Gong","year":"2025","unstructured":"Gong Z, Chen S, Dai Q, Feng Y, Wang J, Zhang J (2025) SCoAMPS: semi-supervised graph contrastive learning based on associative memory network and pseudo-label similarity[J]. Big Data Min Analytics 8(2):273\u2013291","journal-title":"Big Data Min Analytics"},{"key":"889_CR15","doi-asserted-by":"crossref","unstructured":"Kang WC, McAuley J (2018) Self-attentive sequential recommendation[C]. In: 2018 IEEE international conference on data mining (ICDM). IEEE, pp 197\u2013206","DOI":"10.1109\/ICDM.2018.00035"},{"key":"889_CR16","doi-asserted-by":"crossref","unstructured":"Sun F, Liu J, Wu J et al (2019) BERT4Rec: sequential recommendation with bidirectional encoder representations from transformer[C]. In: Proceedings of the 28th ACM International Conference on Information and Knowledge Management. ACM, New York, pp 1441\u20131450","DOI":"10.1145\/3357384.3357895"},{"issue":"4","key":"889_CR17","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3620677","volume":"15","author":"Y Liu","year":"2024","unstructured":"Liu Y, Zhou X, Kou H, Zhao Y, Xu X, Zhang X, Qi L (2024) Privacy-preserving point-of-interest recommendation based on simplified graph convolutional network for geological traveling. ACM Trans Intell Syst Technol 15(4):1\u201317","journal-title":"ACM Trans Intell Syst Technol"},{"key":"889_CR18","doi-asserted-by":"crossref","unstructured":"Fan X, Liu Z, Lian J et al (2021) Lighter and better: low-rank decomposed self-attention networks for next-item recommendation[C]. In: Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval. ACM, New York, pp 1733\u20131737","DOI":"10.1145\/3404835.3462978"},{"key":"889_CR19","doi-asserted-by":"crossref","unstructured":"Zhou P, Ye Q, Xie Y et al (2023) Attention calibration for transformer-based sequential recommendation[C]. In: Proceedings of the 32nd ACM International Conference on Information and Knowledge Management. ACM, New York, pp 3595\u20133605","DOI":"10.1145\/3583780.3614785"},{"issue":"4","key":"889_CR20","doi-asserted-by":"publisher","first-page":"971","DOI":"10.26599\/TST.2023.9010061","volume":"29","author":"Y Peng","year":"2024","unstructured":"Peng Y, Xu S, Chen Q, Huang W, Huang Y (2024) A novel popularity extraction method applied in session-based recommendation[J]. Tsinghua Sci Technol 29(4):971\u2013984","journal-title":"Tsinghua Sci Technol"},{"key":"889_CR21","doi-asserted-by":"crossref","unstructured":"Fan Z, Liu Z, Wang Y et al (2022) Sequential recommendation via stochastic self-attention[C]. In: Proceedings of the ACM web conference 2022, pp 2036\u20132047","DOI":"10.1145\/3485447.3512077"},{"key":"889_CR22","first-page":"1","volume":"16139","author":"NGUYEN T T S","year":"2026","unstructured":"Nguyen TTS, Ho DPN, Ho DHT (2026) Self-attention based sequential recommendation systems improved with reviews topic modeling[J]. Lect Notes Comput Sci 16139:1\u201315","journal-title":"Lect Notes Comput Sci"},{"key":"889_CR23","doi-asserted-by":"crossref","unstructured":"Li Y, Chen T, Zhang PF et al (2021) Lightweight self-attentive sequential recommendation[C]. In: Proceedings of the 30th ACM International Conference on Information and Knowledge Management. ACM, New York, pp 967\u2013977","DOI":"10.1145\/3459637.3482448"},{"key":"889_CR24","doi-asserted-by":"crossref","unstructured":"Harte J, Zorgdrager W, Louridas P et al (2023) Leveraging large language models for sequential recommendation[C]. In: Proceedings of the 17th ACM Conference on Recommender Systems. ACM, New York, pp 1092\u20131098","DOI":"10.1145\/3604915.3610639"},{"key":"889_CR25","doi-asserted-by":"crossref","unstructured":"Pan YZ, Liu J, Yu HL et al (2023) Understanding and modeling passive-negative feedback for short-video sequential recommendation[C]. In: Proceedings of the 17th ACM Conference on Recommender Systems. ACM, New York, pp 991\u2013997","DOI":"10.1145\/3604915.3608814"},{"issue":"3","key":"889_CR26","doi-asserted-by":"publisher","first-page":"964","DOI":"10.26599\/BDMA.2024.9020009","volume":"7","author":"K Taha","year":"2024","unstructured":"Taha K, Yoo PD, Yeun C, Taha A (2024) Empirical and experimental perspectives on big data in recommendation systems: a comprehensive survey[J]. Big Data Min Analytics 7(3):964\u20131014","journal-title":"Big Data Min Analytics"},{"key":"889_CR27","unstructured":"GE Y, LIU S, GAO R et al (2023) Reinforcing user retention in a billion scale short video recommender system[C]. In: Companion Proceedings of the ACM Web Conference 2023. ACM, New York, pp 1\u20135"},{"key":"889_CR28","doi-asserted-by":"publisher","first-page":"1281614","DOI":"10.3389\/fdata.2023.1281614","volume":"6","author":"S Lubos","year":"2023","unstructured":"Lubos S, Felfernig A, Tautschnig M (2023) An overview of video recommender systems: state-of-the-art and research issues[J]. Front Big Data 6:1281614","journal-title":"Front big Data"},{"key":"889_CR29","doi-asserted-by":"crossref","unstructured":"Kang Y, Lin H, Yang M et al (2024) UMAIR-FPS: user-aware multi-modal animation illustration recommendation fusion with painting style[C]. In: International Conference on Database Systems for Advanced Applications. Springer Nature Singapore, Singapore, pp 483\u2013494","DOI":"10.1007\/978-981-97-5555-4_34"},{"issue":"21","key":"889_CR30","doi-asserted-by":"publisher","first-page":"32329","DOI":"10.1007\/s11042-023-14710-9","volume":"82","author":"B Soni","year":"2023","unstructured":"Soni B, Thakuria D, Nath N et al (2023) RikoNet: a novel anime recommendation engine[J]. Multimedia Tools Appl 82(21):32329\u201332348","journal-title":"Multimedia Tools Appl"},{"key":"889_CR31","doi-asserted-by":"publisher","DOI":"10.1145\/3771772","author":"L Qi","year":"2025","unstructured":"Qi L, Xie J, Hu C, Xu X, Xiang H, Dai H, Dou W (2025) Knowledge-driven reasoning for compatible and interpretable API recommendation via teacher LLM distillation. ACM Trans Inform Syst. https:\/\/doi.org\/10.1145\/3771772","journal-title":"ACM Trans Inform Syst"},{"key":"889_CR32","doi-asserted-by":"publisher","unstructured":"Qi L, Yan B, Wang W, Hu C, Dai F, Xu X, Dou W, Zhou X (2025) ST-BernT: a spatiotemporal \u03bb-bernstein graph convolutional network with transformer for multi-site air quality prediction in distributed unmanned agent systems. IEEE Internet Things J. https:\/\/doi.org\/10.1109\/JIOT.2025.3617922","DOI":"10.1109\/JIOT.2025.3617922"},{"issue":"1","key":"889_CR33","doi-asserted-by":"publisher","first-page":"34","DOI":"10.26599\/TST.2024.9010024","volume":"30","author":"B Feng","year":"2025","unstructured":"Feng B, Ding Z (2025) Application-oriented cloud workload prediction: a survey and new perspectives[J]. Tsinghua Sci Technol 30(1):34\u201354","journal-title":"Tsinghua Sci Technol"},{"key":"889_CR34","doi-asserted-by":"crossref","unstructured":"Gu R, Wang S, Dai H, Chen X, Wang Z, Bao W, Chen G (2024). Fluid-shuttle: efficient cloud data transmission based on serverless computing compression. IEEE\/ACM Trans Netw 32(6):4554\u20134569","DOI":"10.1109\/TNET.2024.3402561"},{"issue":"3","key":"889_CR35","doi-asserted-by":"publisher","first-page":"575","DOI":"10.26599\/BDMA.2024.9020084","volume":"8","author":"X Huang","year":"2025","unstructured":"Huang X, Gu R, Huang Y (2025) Towards efficient serverless mapreduce computing on cloud-native platforms[J]. Big Data Min Analytics 8(3):575\u2013591","journal-title":"Big Data Min Analytics"},{"issue":"5","key":"889_CR36","doi-asserted-by":"publisher","first-page":"2227","DOI":"10.26599\/TST.2024.9010105","volume":"30","author":"J Xu","year":"2025","unstructured":"Xu J, Xiang H, Zang S et al (2025) A DQN-based edge offloading method for smart city pollution control[J]. Tsinghua Sci Technol 30(5):2227\u20132242","journal-title":"Tsinghua Sci Technol"},{"issue":"10","key":"889_CR37","doi-asserted-by":"publisher","first-page":"3161","DOI":"10.1109\/JSAC.2023.3310077","volume":"41","author":"L Qi","year":"2023","unstructured":"Qi L, Xu X, Wu X, Ni Q, Yuan Y, Zhang X (2023) Digital-twin-enabled 6\u00a0g mobile network video streaming using mobile crowdsourcing. IEEE J Sel Areas Commun 41(10):3161\u20133174","journal-title":"IEEE J Sel Areas Commun"},{"issue":"10","key":"889_CR38","doi-asserted-by":"publisher","first-page":"10024","DOI":"10.1109\/TMC.2025.3567459","volume":"24","author":"X Xu","year":"2025","unstructured":"Xu X, Hu Y, Cui G, Qi L, Dou W, Cai Z (2025) CADEC: a combinatorial auction for dynamic distributed DNN inference scheduling in edge-cloud networks. IEEE Trans Mob Comput 24(10):10024\u201310041","journal-title":"IEEE Trans Mob Comput"},{"issue":"4","key":"889_CR39","doi-asserted-by":"publisher","first-page":"1065","DOI":"10.26599\/BDMA.2024.9020022","volume":"7","author":"C Luo","year":"2024","unstructured":"Luo C, Zhang J, Guo J et al (2024) Energy efficiency maximization in RISs-assisted UAVs-based edge computing network using deep reinforcement learning[J]. Big Data Min Analytics 7(4):1065\u20131083","journal-title":"Big Data Min Analytics"},{"key":"889_CR40","unstructured":"https:\/\/opensource.foursquare.com\/os-places\/"},{"key":"889_CR41","doi-asserted-by":"crossref","unstructured":"Rendle S, Freudenthaler C, Schmidt-Thieme L (2010) Factorizing personalized markov chains for next-basket recommendation[C]. In: Proceedings of the 19th International Conference on World Wide Web (WWW 2010). ACM, Raleigh, NC, USA","DOI":"10.1145\/1772690.1772773"},{"key":"889_CR42","unstructured":"Hidasi B, Karatzoglou A, Baltrunas L, Tikk D (2016) Session-based recommendations with recurrent neural networks[C]. In: International Conference on Learning Representations (ICLR 2016). ICLR, San Juan, Puerto Rico"},{"key":"889_CR43","doi-asserted-by":"crossref","unstructured":"Kang WC, McAuley J (2018) Self-attentive sequential recommendation[C]. In: 2018 IEEE International Conference on Data Mining (ICDM). IEEE, Singapore","DOI":"10.1109\/ICDM.2018.00035"},{"key":"889_CR44","doi-asserted-by":"crossref","unstructured":"Liu Q, Zeng Y, Mokhosi R, Zhang HSTAMP (2018) Short-term attention\/memory priority model for session-based recommendation[C]. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining (KDD 2018). ACM, London, UK","DOI":"10.1145\/3219819.3219950"},{"key":"889_CR45","doi-asserted-by":"crossref","unstructured":"Li Y, Chen T, Zhang PF, Yin H (2021) Lightweight self-attentive sequential recommendation[C]. In: Proceedings of the 30th ACM International Conference on Information and Knowledge Management (CIKM 2021). ACM, Gold Coast, Australia","DOI":"10.1145\/3459637.3482448"},{"issue":"6","key":"889_CR46","first-page":"5444","volume":"35","author":"L Qi","year":"2023","unstructured":"Qi L, Lin W, Zhang X, Dou W, Xu X, Chen J (2023) A correlation graph based approach for personalized and compatible web apis recommendation in mobile app development. IEEE Trans Knowl Data Eng 35(6):5444\u20135457","journal-title":"IEEE Trans Knowl Data Eng"},{"issue":"6","key":"889_CR47","first-page":"1","volume":"43","author":"F Wang","year":"2025","unstructured":"Wang F, Qi L, Liu W, Yu B, Chen J, Xu Y (2025) Inter-and intra-similarity preserved counterfactual incentive effect estimation for recommendation systems. ACM Trans Inform Syst 43(6):1\u201324","journal-title":"ACM Trans Inform Syst"},{"issue":"2","key":"889_CR48","doi-asserted-by":"publisher","first-page":"771","DOI":"10.1109\/TCSS.2022.3168595","volume":"10","author":"S Wu","year":"2022","unstructured":"Wu S, Shen S, Xu X, Chen Y, Zhou X, Liu D, Qi L (2022) Popularity-aware and diverse web APIs recommendation based on correlation graph. IEEE Trans Comput Social Syst 10(2):771\u2013782","journal-title":"IEEE Trans Comput Social Syst"}],"container-title":["Journal of Cloud Computing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/article\/10.1186\/s13677-026-00889-3","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s13677-026-00889-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s13677-026-00889-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,3,19]],"date-time":"2026-03-19T10:47:27Z","timestamp":1773917247000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1186\/s13677-026-00889-3"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,3,18]]},"references-count":48,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2026,12]]}},"alternative-id":["889"],"URL":"https:\/\/doi.org\/10.1186\/s13677-026-00889-3","relation":{},"ISSN":["2192-113X"],"issn-type":[{"value":"2192-113X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,3,18]]},"assertion":[{"value":"7 January 2026","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"11 March 2026","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"18 March 2026","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"Not applicable.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval and consent to participate"}},{"value":"The authors all consent for publication of this paper.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for publication"}},{"value":"The authors declare no competing interests.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"43"}}