{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,19]],"date-time":"2026-05-19T20:02:58Z","timestamp":1779220978597,"version":"3.51.4"},"reference-count":40,"publisher":"Springer Science and Business Media LLC","issue":"4","license":[{"start":{"date-parts":[[2023,12,16]],"date-time":"2023-12-16T00:00:00Z","timestamp":1702684800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,12,16]],"date-time":"2023-12-16T00:00:00Z","timestamp":1702684800000},"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":["Front. Comput. Sci."],"published-print":{"date-parts":[[2024,8]]},"DOI":"10.1007\/s11704-023-2441-1","type":"journal-article","created":{"date-parts":[[2023,12,16]],"date-time":"2023-12-16T04:02:15Z","timestamp":1702699335000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":11,"title":["Representation learning: serial-autoencoder for personalized recommendation"],"prefix":"10.1007","volume":"18","author":[{"given":"Yi","family":"Zhu","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yishuai","family":"Geng","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yun","family":"Li","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jipeng","family":"Qiang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xindong","family":"Wu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2023,12,16]]},"reference":[{"issue":"23","key":"2441_CR1","doi-asserted-by":"publisher","first-page":"12408","DOI":"10.3390\/app122312408","volume":"12","author":"Y Geng","year":"2022","unstructured":"Geng Y, Zhu Y, Li Y, Sun X, Li B. Multi-feature extension via semi-autoencoder for personalized recommendation. Applied Sciences, 2022, 12(23): 12408","journal-title":"Applied Sciences"},{"key":"2441_CR2","doi-asserted-by":"publisher","first-page":"107005","DOI":"10.1016\/j.asoc.2020.107005","volume":"101","author":"Y Liu","year":"2021","unstructured":"Liu Y, Liang C, Chiclana F, Wu J. A knowledge coverage-based trust propagation for recommendation mechanism in social network group decision making. Applied Soft Computing, 2021, 101: 107005","journal-title":"Applied Soft Computing"},{"key":"2441_CR3","first-page":"100047","volume":"3","author":"N W Rahayu","year":"2022","unstructured":"Rahayu N W, Ferdiana R, Kusumawardani S S. A systematic review of ontology use in E-Learning recommender system. Computers and Education: Artificial Intelligence, 2022, 3: 100047","journal-title":"Computers and Education: Artificial Intelligence"},{"issue":"2","key":"2441_CR4","doi-asserted-by":"publisher","first-page":"100027","DOI":"10.1016\/j.jjimei.2021.100027","volume":"1","author":"D P D Rajendran","year":"2021","unstructured":"Rajendran D P D, Sundarraj R P. Using topic models with browsing history in hybrid collaborative filtering recommender system: experiments with user ratings. International Journal of Information Management Data Insights, 2021, 1(2): 100027","journal-title":"International Journal of Information Management Data Insights"},{"key":"2441_CR5","doi-asserted-by":"publisher","first-page":"101019","DOI":"10.1016\/j.elerap.2020.101019","volume":"45","author":"N Ghasemi","year":"2021","unstructured":"Ghasemi N, Momtazi S. Neural text similarity of user reviews for improving collaborative filtering recommender systems. Electronic Commerce Research and Applications, 2021, 45: 101019","journal-title":"Electronic Commerce Research and Applications"},{"issue":"4","key":"2441_CR6","doi-asserted-by":"publisher","first-page":"986","DOI":"10.1109\/TCSS.2021.3064213","volume":"9","author":"F Wang","year":"2022","unstructured":"Wang F, Zhu H, Srivastava G, Li S, Khosravi M R, Qi L. Robust collaborative filtering recommendation with user-item-trust records. IEEE Transactions on Computational Social Systems, 2022, 9(4): 986\u2013996","journal-title":"IEEE Transactions on Computational Social Systems"},{"issue":"2","key":"2441_CR7","doi-asserted-by":"publisher","first-page":"31","DOI":"10.1145\/3434767","volume":"15","author":"Y Zhu","year":"2021","unstructured":"Zhu Y, Li L, Wu X. Stacked convolutional sparse auto-encoders for representation learning. ACM Transactions on Knowledge Discovery from Data, 2021, 15(2): 31","journal-title":"ACM Transactions on Knowledge Discovery from Data"},{"key":"2441_CR8","doi-asserted-by":"publisher","first-page":"115825","DOI":"10.1016\/j.eswa.2021.115825","volume":"186","author":"Y Zhu","year":"2021","unstructured":"Zhu Y, Wu X, Qiang J, Yuan Y, Li Y. Representation learning with collaborative autoencoder for personalized recommendation. Expert Systems with Applications, 2021, 186: 115825","journal-title":"Expert Systems with Applications"},{"issue":"4","key":"2441_CR9","doi-asserted-by":"publisher","first-page":"2503","DOI":"10.1007\/s00521-021-05933-8","volume":"34","author":"M Yu","year":"2022","unstructured":"Yu M, Quan T, Peng Q, Yu X, Liu L. A model-based collaborate filtering algorithm based on stacked AutoEncoder. Neural Computing and Applications, 2022, 34(4): 2503\u20132511","journal-title":"Neural Computing and Applications"},{"key":"2441_CR10","doi-asserted-by":"crossref","unstructured":"Zhu H, Qian Z, Ye Z, Zhang D. An approach to rating prediction for personality recommendation via attention mechanism and denoising autoencoder. In: Proceedings of 2022 International Conference on Machine Learning, Cloud Computing and Intelligent Mining. 2022, 463\u2013469","DOI":"10.1109\/MLCCIM55934.2022.00085"},{"issue":"5","key":"2441_CR11","doi-asserted-by":"publisher","first-page":"97","DOI":"10.1145\/3535101","volume":"55","author":"S Wu","year":"2023","unstructured":"Wu S, Sun F, Zhang W, Xie X, Cui B. Graph neural networks in recommender systems: a survey. ACM Computing Surveys, 2023, 55(5): 97","journal-title":"ACM Computing Surveys"},{"issue":"1","key":"2441_CR12","doi-asserted-by":"publisher","first-page":"111201","DOI":"10.1007\/s11432-022-3538-4","volume":"66","author":"Y Yan","year":"2023","unstructured":"Yan Y, Cheng D, Feng J E, Li H, Yue J. Survey on applications of algebraic state space theory of logical systems to finite state machines. Science China Information Sciences, 2023, 66(1): 111201","journal-title":"Science China Information Sciences"},{"key":"2441_CR13","doi-asserted-by":"publisher","first-page":"9454","DOI":"10.1109\/ACCESS.2018.2789866","volume":"6","author":"L Zhang","year":"2018","unstructured":"Zhang L, Luo T, Zhang F, Wu Y. A recommendation model based on deep neural network. IEEE Access, 2018, 6: 9454\u20139463","journal-title":"IEEE Access"},{"issue":"9","key":"2441_CR14","first-page":"1457","volume":"5","author":"P O Hoyer","year":"2004","unstructured":"Hoyer P O. Non-negative matrix factorization with sparseness constraints. Journal of Machine Learning Research, 2004, 5(9): 1457\u20131469","journal-title":"Journal of Machine Learning Research"},{"key":"2441_CR15","doi-asserted-by":"crossref","unstructured":"Koren Y. Factorization meets the neighborhood: a multifaceted collaborative filtering model. In: Proceedings of the 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 2008, 426\u2013434","DOI":"10.1145\/1401890.1401944"},{"key":"2441_CR16","doi-asserted-by":"crossref","unstructured":"Rashed A, Grabocka J, Schmidt-Thieme L. Attribute-aware non-linear co-embeddings of graph features. In: Proceedings of the 13th ACM Conference on Recommender Systems. 2019, 314\u2013321","DOI":"10.1145\/3298689.3346999"},{"key":"2441_CR17","doi-asserted-by":"crossref","unstructured":"He X, Deng K, Wang X, Li Y, Zhang Y, Wang M. 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. 2020, 639\u2013648","DOI":"10.1145\/3397271.3401063"},{"key":"2441_CR18","doi-asserted-by":"crossref","unstructured":"Lu Y, Fang Y, Shi C. Meta-learning on heterogeneous information networks for cold-start recommendation. In: Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. 2020, 1563\u20131573","DOI":"10.1145\/3394486.3403207"},{"key":"2441_CR19","doi-asserted-by":"crossref","unstructured":"Yu Z, Lian J, Mahmoody A, Liu G, Xie X. Adaptive user modeling with long and short-term preferences for personalized recommendation. In: Proceedings of the 28th International Joint Conference on Artificial Intelligence. 2019, 4213\u20134219","DOI":"10.24963\/ijcai.2019\/585"},{"key":"2441_CR20","doi-asserted-by":"crossref","unstructured":"Cheng H T, Koc L, Harmsen J, Shaked T, Chandra T, Aradhye H, Anderson G, Corrado G, Chai W, Ispir M, Anil R, Haque Z, Hong L, Jain V, Liu X, Shah H. Wide & deep learning for recommender systems. In: Proceedings of the 1st Workshop on Deep Learning for Recommender Systems. 2016, 7\u201310","DOI":"10.1145\/2988450.2988454"},{"key":"2441_CR21","doi-asserted-by":"crossref","unstructured":"He X, Liao L, Zhang H, Nie L, Hu X, Chua T S. Neural collaborative filtering. In: Proceedings of the 26th International Conference on World Wide Web. 2017, 173\u2013182","DOI":"10.1145\/3038912.3052569"},{"key":"2441_CR22","doi-asserted-by":"crossref","unstructured":"He X, Chua T S. Neural factorization machines for sparse predictive analytics. In: Proceedings of the 40th International ACM SIGIR Conference on Research and Development in Information Retrieval. 2017, 355\u2013364","DOI":"10.1145\/3077136.3080777"},{"key":"2441_CR23","doi-asserted-by":"publisher","first-page":"69009","DOI":"10.1109\/ACCESS.2018.2880197","volume":"6","author":"R Mu","year":"2018","unstructured":"Mu R. A survey of recommender systems based on deep learning. IEEE Access, 2018, 6: 69009\u201369022","journal-title":"IEEE Access"},{"key":"2441_CR24","unstructured":"Yang S, Wang Y, Chu X. A survey of deep learning techniques for neural machine translation. 2020, arXiv preprint arXiv: 2002.07526"},{"key":"2441_CR25","doi-asserted-by":"publisher","first-page":"101360","DOI":"10.1016\/j.csl.2022.101360","volume":"75","author":"A S Subramanian","year":"2022","unstructured":"Subramanian A S, Weng C, Watanabe S, Yu M, Yu D. Deep learning based multi-source localization with source splitting and its effectiveness in multi-talker speech recognition. Computer Speech & Language, 2022, 75: 101360","journal-title":"Computer Speech & Language"},{"key":"2441_CR26","doi-asserted-by":"publisher","first-page":"106744","DOI":"10.1016\/j.knosys.2021.106744","volume":"215","author":"Y Zhu","year":"2021","unstructured":"Zhu Y, Lin Q, Lu H, Shi K, Qiu P, Niu Z. Recommending scientific paper via heterogeneous knowledge embedding based attentive recurrent neural networks. Knowledge-Based Systems, 2021, 215: 106744","journal-title":"Knowledge-Based Systems"},{"issue":"1","key":"2441_CR27","doi-asserted-by":"publisher","first-page":"15","DOI":"10.18267\/j.aip.167","volume":"11","author":"P M Alamdari","year":"2022","unstructured":"Alamdari P M, Navimipour N J, Hosseinzadeh M, Safaei A A, Darwesh A. Image-based product recommendation method for E-commerce applications using convolutional neural networks. Acta Informatica Pragensia, 2022, 11(1): 15\u201335","journal-title":"Acta Informatica Pragensia"},{"issue":"5","key":"2441_CR28","doi-asserted-by":"publisher","first-page":"1607","DOI":"10.1007\/s00521-020-05085-1","volume":"33","author":"H Tahmasebi","year":"2021","unstructured":"Tahmasebi H, Ravanmehr R, Mohamadrezaei R. Social movie recommender system based on deep autoencoder network using Twitter data. Neural Computing and Applications, 2021, 33(5): 1607\u20131623","journal-title":"Neural Computing and Applications"},{"key":"2441_CR29","doi-asserted-by":"crossref","unstructured":"Askari B, Szlichta J, Salehi-Abari A. Variational autoencoders for Top-K recommendation with implicit feedback. In: Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval. 2021, 2061\u20132065","DOI":"10.1145\/3404835.3462986"},{"key":"2441_CR30","doi-asserted-by":"crossref","unstructured":"Zhu Y, Chen Z. Mutually-regularized dual collaborative variational auto-encoder for recommendation systems. In: Proceedings of the ACM Web Conference 2022. 2022, 2379\u20132387","DOI":"10.1145\/3485447.3512110"},{"key":"2441_CR31","doi-asserted-by":"crossref","unstructured":"Zhang S, Yao L, Xu X, Wang S, Zhu L. Hybrid collaborative recommendation via semi-AutoEncoder. In: Proceedings of the 24th International Conference on Neural Information Processing. 2017, 185\u2013193","DOI":"10.1007\/978-3-319-70087-8_20"},{"issue":"6","key":"2441_CR32","doi-asserted-by":"publisher","first-page":"6196","DOI":"10.1007\/s10489-021-02647-1","volume":"52","author":"Y Yang","year":"2022","unstructured":"Yang Y, Zhu Y, Li Y. Personalized recommendation with knowledge graph via dual-autoencoder. Applied Intelligence, 2022, 52(6): 6196\u20136207","journal-title":"Applied Intelligence"},{"issue":"1","key":"2441_CR33","doi-asserted-by":"publisher","first-page":"135","DOI":"10.3390\/electronics9010135","volume":"9","author":"S Nurmaini","year":"2020","unstructured":"Nurmaini S, Darmawahyuni A, Mukti A N S, Rachmatullah M N, Firdaus F, Tutuko B. Deep learning-based stacked denoising and autoencoder for ECG heartbeat classification. Electronics, 2020, 9(1): 135","journal-title":"Electronics"},{"issue":"2","key":"2441_CR34","doi-asserted-by":"publisher","first-page":"430","DOI":"10.1007\/s11704-018-8052-6","volume":"14","author":"G Zhang","year":"2020","unstructured":"Zhang G, Liu Y, Jin X. A survey of autoencoder-based recommender systems. Frontiers of Computer Science, 2020, 14(2): 430\u2013450","journal-title":"Frontiers of Computer Science"},{"key":"2441_CR35","doi-asserted-by":"crossref","unstructured":"Xie Z, Liu C, Zhang Y, Lu H, Wang D, Ding Y. Adversarial and contrastive variational autoencoder for sequential recommendation. In: Proceedings of the Web Conference 2021. 2021, 449\u2013459","DOI":"10.1145\/3442381.3449873"},{"key":"2441_CR36","doi-asserted-by":"publisher","first-page":"108723","DOI":"10.1016\/j.ymssp.2021.108723","volume":"169","author":"D Jana","year":"2022","unstructured":"Jana D, Patil J, Herkal S, Nagarajaiah S, Duenas-Osorio L. CNN and Convolutional Autoencoder (CAE) based real-time sensor fault detection, localization, and correction. Mechanical Systems and Signal Processing, 2022, 169: 108723","journal-title":"Mechanical Systems and Signal Processing"},{"key":"2441_CR37","doi-asserted-by":"crossref","unstructured":"Zhu Y, Dong B, Sha Z. Personalized recommendation based on entity attributes and graph features. In: Proceedings of 2021 IEEE International Conference on Big Knowledge. 2021, 7\u201314","DOI":"10.1109\/ICKG52313.2021.00011"},{"key":"2441_CR38","doi-asserted-by":"publisher","first-page":"891265","DOI":"10.3389\/fgene.2022.891265","volume":"13","author":"Y Geng","year":"2022","unstructured":"Geng Y, Xiao X, Sun X, Zhu Y. Representation learning: Recommendation with knowledge graph via triple-autoencoder. Frontiers in Genetics, 2022, 13: 891265","journal-title":"Frontiers in Genetics"},{"key":"2441_CR39","unstructured":"Dooms S, De Pessemier T, Martens L. MovieTweetings: a movie rating dataset collected from twitter. In: Proceedings of the Workshop on Crowdsourcing and Human Computation for Recommender Systems, Held in Conjunction with the 7th ACM Conference on Recommender Systems. 2013, 43"},{"issue":"1","key":"2441_CR40","first-page":"2699","volume":"13","author":"J Lee","year":"2012","unstructured":"Lee J, Sun M, Lebanon G. PREA: personalized recommendation algorithms toolkit. The Journal of Machine Learning Research, 2012, 13(1): 2699\u20132703","journal-title":"The Journal of Machine Learning Research"}],"container-title":["Frontiers of Computer Science"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11704-023-2441-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11704-023-2441-1\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11704-023-2441-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,9,19]],"date-time":"2025-09-19T19:39:40Z","timestamp":1758310780000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11704-023-2441-1"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,12,16]]},"references-count":40,"journal-issue":{"issue":"4","published-print":{"date-parts":[[2024,8]]}},"alternative-id":["2441"],"URL":"https:\/\/doi.org\/10.1007\/s11704-023-2441-1","relation":{},"ISSN":["2095-2228","2095-2236"],"issn-type":[{"value":"2095-2228","type":"print"},{"value":"2095-2236","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,12,16]]},"assertion":[{"value":"10 July 2022","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"17 April 2023","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"16 December 2023","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}],"article-number":"184316"}}