{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,4]],"date-time":"2026-07-04T13:21:40Z","timestamp":1783171300616,"version":"3.54.6"},"reference-count":46,"publisher":"Springer Science and Business Media LLC","issue":"2","license":[{"start":{"date-parts":[[2026,2,17]],"date-time":"2026-02-17T00:00:00Z","timestamp":1771286400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2026,2,17]],"date-time":"2026-02-17T00:00:00Z","timestamp":1771286400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"name":"the Key Research and Development Program of Henan","award":["251111212000"],"award-info":[{"award-number":["251111212000"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Evolving Systems"],"published-print":{"date-parts":[[2026,6]]},"DOI":"10.1007\/s12530-025-09783-w","type":"journal-article","created":{"date-parts":[[2026,2,17]],"date-time":"2026-02-17T11:14:17Z","timestamp":1771326857000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Multi-level cross-view contrastive learning for enhanced item intention-aware recommender system"],"prefix":"10.1007","volume":"17","author":[{"given":"Shuqin","family":"Zhang","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Dashuang","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yuanqing","family":"Xia","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hui","family":"Zhao","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jizhao","family":"Liu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,2,17]]},"reference":[{"key":"9783_CR5","doi-asserted-by":"publisher","unstructured":"Cao Y, Wang X, He X, Hu Z, Chua T-S (2019) Unifying knowledge graph learning and recommendation: towards a better understanding of user preferences. In: The World Wide Web Conference. WWW \u201919, pp. 151\u2013161. Association for Computing Machinery, New York, NY, USA. https:\/\/doi.org\/10.1145\/3308558.3313705","DOI":"10.1145\/3308558.3313705"},{"key":"9783_CR1","doi-asserted-by":"publisher","DOI":"10.1145\/3643806","author":"J Cao","year":"2024","unstructured":"Cao J, Fang J, Meng Z, Liang S (2024) Knowledge graph embedding: a survey from the perspective of representation spaces. ACM Comput Surv. https:\/\/doi.org\/10.1145\/3643806","journal-title":"ACM Comput Surv"},{"key":"9783_CR2","first-page":"1989","volume":"10","author":"J Chen","year":"2009","unstructured":"Chen J, Fang H-R, Saad Y (2009) Fast approximate kNN graph construction for high dimensional data via recursive lanczos bisection. J Mach Learn Res 10:1989\u20132012","journal-title":"J Mach Learn Res"},{"key":"9783_CR3","unstructured":"Chen T, Kornblith S, Norouzi M, Hinton G (2020) A simple framework for contrastive learning of visual representations. arXiv:2002.05709"},{"key":"9783_CR6","unstructured":"Chen Y, Wu L, Zaki MJ (2020) Iterative deep graph learning for graph neural networks: better and robust node embeddings arXiv:arXiv:2006.13009 [cs.LG]"},{"key":"9783_CR7","doi-asserted-by":"crossref","unstructured":"Daruna AA, Gupta M, Sridharan M, Chernova S (2021) Continual learning of knowledge graph embeddings. CoRR arXiv:2101.05850","DOI":"10.1109\/LRA.2021.3056071"},{"key":"9783_CR8","doi-asserted-by":"crossref","unstructured":"Dingxian Xu C, He X, Cao Y, Chua T-S (2019) Explainable reasoning over knowledge graphs for recommendation. In: Proceedings of the AAAI Conference on Artificial Intelligence, 33:5329\u20135336","DOI":"10.1609\/aaai.v33i01.33015329"},{"key":"9783_CR9","unstructured":"Glorot X, Bengio Y (2010) Understanding the difficulty of training deep feedforward neural networks. In: Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics, pp. 249\u2013256. JMLR Workshop and Conference Proceedings"},{"issue":"10","key":"9783_CR10","doi-asserted-by":"publisher","first-page":"10281","DOI":"10.1109\/TKDE.2023.3251897","volume":"35","author":"M Gao","year":"2023","unstructured":"Gao M, Li J-Y, Chen C-H, Li Y, Zhang J, Zhan Z-H (2023) Enhanced multi-task learning and knowledge graph-based recommender system. IEEE Trans Knowl Data Eng 35(10):10281\u201310294. https:\/\/doi.org\/10.1109\/TKDE.2023.3251897","journal-title":"IEEE Trans Knowl Data Eng"},{"key":"9783_CR11","doi-asserted-by":"crossref","unstructured":"He K, Fan H, Wu Y, Xie S, Girshick RB (2019) Momentum contrast for unsupervised visual representation learning. CoRR arXiv:1911.05722","DOI":"10.1109\/CVPR42600.2020.00975"},{"key":"9783_CR12","unstructured":"Hassani K, Khasahmadi AH (2020) Contrastive multi-view representation learning on graphs. In: International Conference on Machine Learning, pp. 4116\u20134126. PMLR"},{"key":"9783_CR13","doi-asserted-by":"publisher","unstructured":"Han J, Moraga C (1995) The influence of the sigmoid function parameters on the speed of backpropagation learning. In: Proceedings of the International Workshop on Artificial Neural Networks: From Natural to Artificial Neural Computation, pp. 195\u2013201. Springer, Berlin, Heidelberg. https:\/\/doi.org\/10.5555\/646366.689307","DOI":"10.5555\/646366.689307"},{"key":"9783_CR14","doi-asserted-by":"publisher","unstructured":"Hahnloser RH, Sarpeshkar R, Mahowald MA, Douglas RJ, Seung HS (2000) Digital selection and analogue amplification coexist in a cortex-inspired silicon circuit. Nature 405(6789):947\u2013951. https:\/\/doi.org\/10.1038\/35016072. Erratum. In: Nature, 2000 Dec 21-28, vol 408(6815), p. 1012","DOI":"10.1038\/35016072"},{"key":"9783_CR15","unstructured":"Kingma DP, Ba J (2017) Adam: a method for stochastic optimization arXiv:1412.6980 [cs.LG]"},{"key":"9783_CR16","doi-asserted-by":"crossref","unstructured":"Kapoor S, Sharma A, R\u00f6der M, Demir C, Ngomo A-CN (2024) Performance evaluation of knowledge graph embedding approaches under non-adversarial attacks. CoRR arXiv:2407.06855 [cs.LG]","DOI":"10.1007\/978-3-031-94575-5_15"},{"key":"9783_CR17","unstructured":"Kipf TN, Welling M (2017) Semi-supervised classification with graph convolutional networks arXiv:1609.02907 [cs.LG]"},{"key":"9783_CR18","doi-asserted-by":"crossref","unstructured":"Linsker R (1988) Self-organization in a perceptual network. Computer","DOI":"10.1109\/2.36"},{"key":"9783_CR20","doi-asserted-by":"publisher","unstructured":"Liu Y, Xia L, Huang C (2024) Selfgnn: Self-supervised graph neural networks for sequential recommendation. In: Proceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval. SIGIR \u201924, pp. 1609\u20131618. Association for Computing Machinery, New York, NY, USA. https:\/\/doi.org\/10.1145\/3626772.3657716","DOI":"10.1145\/3626772.3657716"},{"key":"9783_CR19","doi-asserted-by":"crossref","unstructured":"Liu J, Ke W, Wang P, Wang J, Gao J, Shang Z, Li G, Xu Z, Ji K, Li Y (2024) Fast and continual knowledge graph embedding via incremental lora. CoRR arXiv:2407.05705 [cs.AI]","DOI":"10.1609\/aaai.v38i8.28722"},{"key":"9783_CR21","unstructured":"Memisevic R, Zach C, Hinton G, Pollefeys M (2010) Gated softmax classification, 1603\u20131611"},{"key":"9783_CR22","doi-asserted-by":"crossref","unstructured":"Peng Z, Huang W, Luo M, Zheng Q, Rong Y, Xu T, Huang J (2020) Graph representation learning via graphical mutual information maximization. arXiv:2002.01169","DOI":"10.1145\/3366423.3380112"},{"key":"9783_CR23","doi-asserted-by":"publisher","unstructured":"Qiu R, Huang Z, Yin H, Wang Z (2022) Contrastive learning for representation degeneration problem in sequential recommendation. In: Proceedings of the Fifteenth ACM International Conference on Web Search and Data Mining. WSDM \u201922, pp. 813\u2013823. Association for Computing Machinery, New York, NY, USA. https:\/\/doi.org\/10.1145\/3488560.3498433","DOI":"10.1145\/3488560.3498433"},{"key":"9783_CR24","unstructured":"Rendle S, Freudenthaler C, Gantner Z, Schmidt-Thieme L (2012) BPR: bayesian personalized ranking from implicit feedback. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence. arXiv:1205.2618"},{"issue":"3","key":"9783_CR25","doi-asserted-by":"publisher","first-page":"1366","DOI":"10.1109\/TKDE.2024.3509480","volume":"37","author":"X Rao","year":"2025","unstructured":"Rao X, Jiang R, Shang S, Chen L, Han P, Yao B, Kalnis P (2025) Next point-of-interest recommendation with adaptive graph contrastive learning. IEEE Trans Knowl Data Eng 37(3):1366\u20131379. https:\/\/doi.org\/10.1109\/TKDE.2024.3509480","journal-title":"IEEE Trans Knowl Data Eng"},{"key":"9783_CR26","unstructured":"Sun Z, Deng Z-H, Nie J-Y, Tang J (2019) Rotate: Knowledge graph embedding by relational rotation in complex space arXiv:1902.10197 [cs.LG]"},{"key":"9783_CR27","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2020.113764","volume":"165","author":"B Shao","year":"2021","unstructured":"Shao B, Li X, Bian G (2021) A survey of research hotspots and frontier trends of recommendation systems from the perspective of knowledge graph. Expert Syst Appl 165:113764. https:\/\/doi.org\/10.1016\/j.eswa.2020.113764","journal-title":"Expert Syst Appl"},{"key":"9783_CR28","unstructured":"Takamoto M, O\u00f1oro-Rubio D, Rim WB, Maruyama T, Kotnis B (2025) Optimal embedding guided negative sample generation for knowledge graph link prediction arXiv:2504.03327 [cs.LG]"},{"key":"9783_CR29","unstructured":"Veli\u010dkovi\u0107 P, Fedus W, Hamilton WL, Li\u00f2 P, Bengio Y, Hjelm RD (2018) Deep graph infomax. CoRR arXiv:1809.10341 [stat.ML]"},{"key":"9783_CR36","doi-asserted-by":"publisher","unstructured":"Wang H, Zhang F, Wang J, Zhao M, Li W, Xie X, Guo M (2018) Ripplenet: Propagating user preferences on the knowledge graph for recommender systems. In: Proceedings of the 27th ACM International Conference on Information and Knowledge Management. CIKM \u201918, pp. 417\u2013426. Association for Computing Machinery, New York, NY, USA. https:\/\/doi.org\/10.1145\/3269206.3271739","DOI":"10.1145\/3269206.3271739"},{"key":"9783_CR30","doi-asserted-by":"publisher","unstructured":"Wang X, He X, Cao Y, Liu M, Chua T-S (2019) Kgat: Knowledge graph attention network for recommendation. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. KDD \u201919, pp. 950\u2013958. Association for Computing Machinery, New York, NY, USA. https:\/\/doi.org\/10.1145\/3292500.3330989","DOI":"10.1145\/3292500.3330989"},{"key":"9783_CR37","doi-asserted-by":"publisher","unstructured":"Wang H, Zhao M, Xie X, Li W, Guo M (2019) Knowledge graph convolutional networks for recommender systems. In: The World Wide Web Conference. WWW \u201919, pp. 3307\u20133313. Association for Computing Machinery, New York, NY, USA. https:\/\/doi.org\/10.1145\/3308558.3313417","DOI":"10.1145\/3308558.3313417"},{"key":"9783_CR38","doi-asserted-by":"publisher","unstructured":"Wang H, Zhang F, Zhang M, Leskovec J, Zhao M, Li W, Wang Z (2019) Knowledge-aware graph neural networks with label smoothness regularization for recommender systems. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. KDD \u201919, pp. 968\u2013977. Association for Computing Machinery, New York, NY, USA. https:\/\/doi.org\/10.1145\/3292500.3330836","DOI":"10.1145\/3292500.3330836"},{"key":"9783_CR33","doi-asserted-by":"publisher","unstructured":"Wang Z, Lin G, Tan H, Chen Q, Liu X (2020) Ckan: Collaborative knowledge-aware attentive network for recommender systems. In: Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval. SIGIR \u201920, pp. 219\u2013228. Association for Computing Machinery, New York, NY, USA. https:\/\/doi.org\/10.1145\/3397271.3401141","DOI":"10.1145\/3397271.3401141"},{"key":"9783_CR31","doi-asserted-by":"publisher","unstructured":"Wang X, Huang T, Wang D, Yuan Y, Liu Z, He X, Chua T-S (2021) Learning intents behind interactions with knowledge graph for recommendation. In: Proceedings of the Web Conference 2021. WWW \u201921, pp. 878\u2013887. Association for Computing Machinery, New York, NY, USA. https:\/\/doi.org\/10.1145\/3442381.3450133","DOI":"10.1145\/3442381.3450133"},{"key":"9783_CR32","doi-asserted-by":"publisher","unstructured":"Wang X, Liu N, Han H, Shi C (2021) Self-supervised heterogeneous graph neural network with co-contrastive learning. In: Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining. KDD \u201921, pp. 1726\u20131736. Association for Computing Machinery, New York, NY, USA. https:\/\/doi.org\/10.1145\/3447548.3467415","DOI":"10.1145\/3447548.3467415"},{"issue":"1\u20132","key":"9783_CR35","first-page":"33","volume":"48","author":"T Wei","year":"2025","unstructured":"Wei T, Yang C, Zheng Y, Zhang J (2025) Collaborative graph contrastive learning for recommendation. J Intell Fuzzy Syst 48(1\u20132):33\u201346","journal-title":"J Intell Fuzzy Syst"},{"key":"9783_CR34","doi-asserted-by":"publisher","unstructured":"Wu J, Wang X, Feng F, He X, Chen L, Lian J, Xie X (2021) Self-supervised graph learning for recommendation. In: Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval. SIGIR \u201921, pp. 726\u2013735. Association for Computing Machinery, New York, NY, USA. https:\/\/doi.org\/10.1145\/3404835.3462862","DOI":"10.1145\/3404835.3462862"},{"key":"9783_CR39","doi-asserted-by":"publisher","unstructured":"Yang Y, Huang C, Xia L, Li C (2022) Knowledge graph contrastive learning for recommendation. In: Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 1434\u20131443. ACM, New York, NY, USA. https:\/\/doi.org\/10.1145\/3477495.3532009","DOI":"10.1145\/3477495.3532009"},{"key":"9783_CR41","doi-asserted-by":"publisher","unstructured":"Yu X, Ren X, Sun Y, Gu Q, Sturt B, Khandelwal U, Norick B, Han J (2014) Personalized entity recommendation: a heterogeneous information network approach. In: Proceedings of the 7th ACM International Conference on Web Search and Data Mining. WSDM \u201914, pp. 283\u2013292. Association for Computing Machinery, New York, NY, USA. https:\/\/doi.org\/10.1145\/2556195.2556259","DOI":"10.1145\/2556195.2556259"},{"key":"9783_CR40","doi-asserted-by":"publisher","unstructured":"Yu R, Li Z, Zhao M, Zhang W, Yang M, Yu J (2023) Multi-view contrastive learning for knowledge-aware recommendation. In: Neural Information Processing: 30th International Conference, ICONIP 2023, Changsha, China, November 20\u201323, 2023, Proceedings, Part V, pp. 211\u2013223. Springer, Berlin, Heidelberg. https:\/\/doi.org\/10.1007\/978-981-99-8073-4_17","DOI":"10.1007\/978-981-99-8073-4_17"},{"key":"9783_CR43","unstructured":"Zhu Y, Xu Y, Yu F, Liu Q, Wu S, Wang L (2020) Deep graph contrastive representation learning. CoRR arXiv:2006.04131"},{"key":"9783_CR45","doi-asserted-by":"crossref","unstructured":"Zhu Y, Xu Y, Yu F, Liu Q, Wu S, Wang L (2020) Graph contrastive learning with adaptive augmentation. CoRR arXiv:2010.14945","DOI":"10.1145\/3442381.3449802"},{"key":"9783_CR46","doi-asserted-by":"publisher","unstructured":"Zhu Y, Xu Y, Yu S, Wang L (2021) Graph contrastive learning with adaptive augmentation. In: Proceedings of the Web Conference 2021. ACM https:\/\/doi.org\/10.1145\/3442381.3449802","DOI":"10.1145\/3442381.3449802"},{"key":"9783_CR42","doi-asserted-by":"crossref","unstructured":"Zhu Y, Hern\u00e1ndez D, He Y, Ding Z, Xiong B, Kharlamov E, Staab S (2025) Predicate-conditional conformalized answer sets for knowledge graph embeddings. CoRR arXiv:2505.16877 [cs.AI]","DOI":"10.18653\/v1\/2025.findings-acl.215"},{"key":"9783_CR47","doi-asserted-by":"publisher","unstructured":"Zhang F, Yuan NJ, Lian D, Xie X, Ma W-Y (2016) Collaborative knowledge base embedding for recommender systems. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD \u201916, pp. 353\u2013362. Association for Computing Machinery, New York, NY, USA. https:\/\/doi.org\/10.1145\/2939672.2939673","DOI":"10.1145\/2939672.2939673"},{"key":"9783_CR48","doi-asserted-by":"publisher","unstructured":"Zhang J, Zhu Y, Liu Q, Wu S, Wang, S, Wang L (2021) Mining latent structures for multimedia recommendation. In: Proceedings of the 29th ACM International Conference on Multimedia, pp. 3872\u20133880. ACM https:\/\/doi.org\/10.1145\/3474085.3475259","DOI":"10.1145\/3474085.3475259"}],"container-title":["Evolving Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s12530-025-09783-w.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s12530-025-09783-w","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s12530-025-09783-w.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,7,4]],"date-time":"2026-07-04T12:58:31Z","timestamp":1783169911000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s12530-025-09783-w"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,2,17]]},"references-count":46,"journal-issue":{"issue":"2","published-print":{"date-parts":[[2026,6]]}},"alternative-id":["9783"],"URL":"https:\/\/doi.org\/10.1007\/s12530-025-09783-w","relation":{},"ISSN":["1868-6478","1868-6486"],"issn-type":[{"value":"1868-6478","type":"print"},{"value":"1868-6486","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,2,17]]},"assertion":[{"value":"2 July 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"4 December 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"17 February 2026","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}],"article-number":"35"}}