{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,21]],"date-time":"2026-05-21T17:03:18Z","timestamp":1779382998554,"version":"3.53.1"},"reference-count":42,"publisher":"Springer Science and Business Media LLC","issue":"6","license":[{"start":{"date-parts":[[2022,12,23]],"date-time":"2022-12-23T00:00:00Z","timestamp":1671753600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2022,12,23]],"date-time":"2022-12-23T00:00:00Z","timestamp":1671753600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61906112"],"award-info":[{"award-number":["61906112"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62076158"],"award-info":[{"award-number":["62076158"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62272286"],"award-info":[{"award-number":["62272286"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100004480","name":"Natural Science Foundation of Shanxi Province","doi-asserted-by":"publisher","award":["201901D211174"],"award-info":[{"award-number":["201901D211174"]}],"id":[{"id":"10.13039\/501100004480","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100004480","name":"Natural Science Foundation of Shanxi Province","doi-asserted-by":"publisher","award":["20210302123468"],"award-info":[{"award-number":["20210302123468"]}],"id":[{"id":"10.13039\/501100004480","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Scientific and Technological Innovation Programs of Higher Education Institutions in Shanxi Province","award":["2019L0008"],"award-info":[{"award-number":["2019L0008"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Int. J. Mach. Learn. & Cyber."],"published-print":{"date-parts":[[2023,6]]},"DOI":"10.1007\/s13042-022-01743-z","type":"journal-article","created":{"date-parts":[[2022,12,23]],"date-time":"2022-12-23T04:20:35Z","timestamp":1671769235000},"page":"2023-2039","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["GCN recommendation model based on the fusion of dynamic multiple-view latent interest topics"],"prefix":"10.1007","volume":"14","author":[{"given":"Feng","family":"Liu","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jian","family":"Liao","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jianxing","family":"Zheng","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Suge","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Deyu","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xin","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2022,12,23]]},"reference":[{"key":"1743_CR1","doi-asserted-by":"crossref","unstructured":"Abdollahi B, Nasraoui O (2016) Explainable matrix factorization for collaborative filtering. In: Proceedings of the 25th International Conference Companion on World Wide Web, Republic and Canton of Geneva, CHE, pp 5\u20136","DOI":"10.1145\/2872518.2889405"},{"key":"1743_CR2","doi-asserted-by":"crossref","unstructured":"Chanpuriya S, Musco C (2020) Infinitewalk: Deep network embeddings as Laplacian embeddings with a nonlinearity. In: Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, New York, NY, USA, KDD \u201920, pp 1325\u20131333","DOI":"10.1145\/3394486.3403185"},{"key":"1743_CR3","doi-asserted-by":"crossref","unstructured":"Chen CM, Wang CJ, Tsai MF et\u00a0al (2019a) Collaborative similarity embedding for recommender systems. In: Proceedings of the International Conference of World Wide Web, pp 2637\u20132643","DOI":"10.1145\/3308558.3313493"},{"key":"1743_CR4","doi-asserted-by":"crossref","unstructured":"Chen L, Wu L, Hong R et\u00a0al (2020a) Revisiting graph based collaborative filtering: a linear residual graph convolutional network approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp 27\u201334","DOI":"10.1609\/aaai.v34i01.5330"},{"key":"1743_CR5","unstructured":"Chen M, Wei Z, Huang Z et\u00a0al (2020b) Simple and deep graph convolutional networks. In: Proceedings of the International Conference on Machine Learning, pp 1725\u20131735"},{"issue":"4","key":"1743_CR6","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3343117","volume":"37","author":"W Chen","year":"2019","unstructured":"Chen W, Cai F, Chen H et al (2019) Joint neural collaborative filtering for recommender systems. ACM Trans Inf Syst 37(4):1\u201330","journal-title":"ACM Trans Inf Syst"},{"issue":"4","key":"1743_CR7","first-page":"1","volume":"38","author":"X Chen","year":"2020","unstructured":"Chen X, Li L, Pan W et al (2020) A survey on heterogeneous one-class collaborative filtering. ACM Trans Inf Syst 38(4):1\u201354","journal-title":"ACM Trans Inf Syst"},{"issue":"2","key":"1743_CR8","doi-asserted-by":"publisher","first-page":"35","DOI":"10.1145\/3447556.3447567","volume":"22","author":"YW Chen","year":"2021","unstructured":"Chen YW, Song Q, Hu X (2021) Techniques for automated machine learning. ACM SIGKDD Explor Newsl (SIGKDD) 22(2):35\u201350","journal-title":"ACM SIGKDD Explor Newsl (SIGKDD)"},{"key":"1743_CR9","volume-title":"Connected: The surprising power of our social networks and how they shape our lives","author":"NA Christakis","year":"2009","unstructured":"Christakis NA, Fowler JH (2009) Connected: The surprising power of our social networks and how they shape our lives. Little, Brown Spark, New York"},{"issue":"2","key":"1743_CR10","doi-asserted-by":"publisher","first-page":"317","DOI":"10.1109\/TKDE.2018.2881260","volume":"32","author":"Q Cui","year":"2020","unstructured":"Cui Q, Wu S, Liu Q et al (2020) Mv-rnn: a multi-view recurrent neural network for sequential recommendation. IEEE Trans Knowl Data Eng 32(2):317\u2013331","journal-title":"IEEE Trans Knowl Data Eng"},{"issue":"4","key":"1743_CR11","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3357154","volume":"37","author":"X Du","year":"2019","unstructured":"Du X, He X, Yuan F et al (2019) Modeling embedding dimension correlations via convolutional neural collaborative filtering. ACM Trans Inf Syst 37(4):1\u201322","journal-title":"ACM Trans Inf Syst"},{"key":"1743_CR12","unstructured":"Fan L, Zhiyong C, Lei Z et\u00a0al (2020) $$a^2$$-gcn: An attribute-aware attentive gcn model for recommendation. IEEE Trans Knowl Data Eng, pp 1\u201311"},{"key":"1743_CR13","doi-asserted-by":"crossref","unstructured":"Grover A, Leskovec J (2016) Node2vec: Scalable feature learning for networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, New York, NY, USA, KDD \u201916, pp 855\u2013864","DOI":"10.1145\/2939672.2939754"},{"key":"1743_CR14","doi-asserted-by":"crossref","unstructured":"He X, Liao L, Zhang H et\u00a0al (2017) Neural collaborative filtering. In: Proceedings of the 26th International Conference on World Wide Web, pp 173\u2013182","DOI":"10.1145\/3038912.3052569"},{"key":"1743_CR15","doi-asserted-by":"crossref","unstructured":"Hu G, Zhang Y, Yang Q (2019a) Transfer meets hybrid: a synthetic approach for cross-domain collaborative filtering with text. In: Proceedings of the International Conference of World Wide Web. Association for Computing Machinery, New York, NY, USA, pp 2822\u20132829","DOI":"10.1145\/3308558.3313543"},{"key":"1743_CR16","doi-asserted-by":"publisher","first-page":"231","DOI":"10.1016\/j.ins.2018.12.003","volume":"479","author":"Q Hu","year":"2019","unstructured":"Hu Q, Han Z, Lin X et al (2019) Learning peer recommendation using attention-driven cnn with interaction tripartite graph. Inf Sci 479:231\u2013249","journal-title":"Inf Sci"},{"key":"1743_CR17","doi-asserted-by":"crossref","unstructured":"Klicpera J, Bojchevski A, G\u00fcnnemann S (2019) Predict then propagate: graph neural networks meet personalized pagerank. In: Proceedings of the International Conference on Learning Representations","DOI":"10.1145\/3394486.3403296"},{"key":"1743_CR18","doi-asserted-by":"crossref","unstructured":"Li Q, Han Z, Wu XM (2018) Deeper insights into graph convolutional networks for semi-supervised learning. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp 3538\u20133545","DOI":"10.1609\/aaai.v32i1.11604"},{"key":"1743_CR19","doi-asserted-by":"crossref","unstructured":"Liu F, Cheng Z, Sun C et\u00a0al (2019) User diverse preference modeling by multimodal attentive metric learning. In: Proceedings of the 27th ACM International Conference on Multimedia, pp 1526\u20131534","DOI":"10.1145\/3343031.3350953"},{"key":"1743_CR20","doi-asserted-by":"crossref","unstructured":"Liu F, Cheng Z, Zhu L et\u00a0al (2021) Interest-aware message-passing gcn for recommendation. In: Proceedings of the International Conference of World Wide Web, pp 1296\u20131305","DOI":"10.1145\/3442381.3449986"},{"key":"1743_CR21","doi-asserted-by":"publisher","first-page":"11","DOI":"10.1016\/j.neucom.2016.12.038","volume":"234","author":"W Liu","year":"2017","unstructured":"Liu W, Wang Z, Liu X et al (2017) A survey of deep neural network architectures and their applications. Neurocomputing 234:11\u201326","journal-title":"Neurocomputing"},{"key":"1743_CR22","doi-asserted-by":"crossref","unstructured":"Mao K, Zhu J, Xiao X et\u00a0al (2021) Ultragcn - ultra simplification of graph convolutional networks for recommendation. In: Proceedings of the ACM International Conference on Information and Knowledge Management, pp 1253\u20131262","DOI":"10.1145\/3459637.3482291"},{"key":"1743_CR23","doi-asserted-by":"crossref","unstructured":"Perozzi B, Al-Rfou R, Skiena S (2014) Deepwalk: Online learning of social representations. In: Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, New York, NY, USA, KDD \u201914, pp 701\u2013710","DOI":"10.1145\/2623330.2623732"},{"key":"1743_CR24","doi-asserted-by":"crossref","unstructured":"Qiu J, Dong Y, Ma H, et\u00a0al (2018) Network embedding as matrix factorization: Unifying deepwalk, line, pte, and node2vec. In: Proceedings of the Eleventh ACM International Conference on Web Search and Data Mining, New York, NY, USA, WSDM \u201918, pp 459\u2013467","DOI":"10.1145\/3159652.3159706"},{"issue":"5","key":"1743_CR25","doi-asserted-by":"publisher","first-page":"1077","DOI":"10.1109\/TPDS.2018.2877363","volume":"30","author":"M Shi","year":"2019","unstructured":"Shi M, Tang Y, Liu J (2019) Functional and contextual attention-based lstm for service recommendation in mashup creation. IEEE Trans Parallel Distrib Syst 30(5):1077\u20131090","journal-title":"IEEE Trans Parallel Distrib Syst"},{"key":"1743_CR26","doi-asserted-by":"crossref","unstructured":"Wang X, He X, Wang M et\u00a0al (2019) Neural graph collaborative filtering. In: Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval, New York, NY, USA, SIGIR\u201919, pp 165\u2013174","DOI":"10.1145\/3331184.3331267"},{"key":"1743_CR27","doi-asserted-by":"crossref","unstructured":"Wang X, Jin H, Zhang A et\u00a0al (2020) Disentangled graph collaborative filtering. In: Proceedings of the 43rd International ACM SIGIR conference on research and development in Information Retrieval, pp 1001\u20131010","DOI":"10.1145\/3397271.3401137"},{"issue":"6684","key":"1743_CR28","doi-asserted-by":"publisher","first-page":"440","DOI":"10.1038\/30918","volume":"393","author":"DJ Watts","year":"1998","unstructured":"Watts DJ, Strogatz SH (1998) Collective dynamics of small-world networks. Nature 393(6684):440\u2013442","journal-title":"Nature"},{"key":"1743_CR29","doi-asserted-by":"crossref","unstructured":"Wei Y, Wang X, Nie L et\u00a0al (2020) Graph-refined convolutional network for multimedia recommendation with implicit feedback. In: Proceedings of the 28th ACM International Conference on Multimedia, pp 3541\u20133549","DOI":"10.1145\/3394171.3413556"},{"key":"1743_CR30","unstructured":"Wu F, Souza HA, Zhang T et\u00a0al (2019) Simplifying graph convolutional networks. In: Proceedings of the International Conference on Machine Learning, pp 6861\u20136871"},{"key":"1743_CR31","doi-asserted-by":"crossref","unstructured":"Wu J, Wang X, Feng F et\u00a0al (2021) Self-supervised graph learning for recommendation. In: Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval, New York, NY, USA, pp 726\u2013735","DOI":"10.1145\/3404835.3462862"},{"key":"1743_CR32","unstructured":"Xiangnan H, Kuan D, Xiang W et\u00a0al (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, pp 639\u2013648"},{"key":"1743_CR33","doi-asserted-by":"crossref","unstructured":"Xiao T, Chen Z, Wang D et\u00a0al (2021) Learning how to propagate messages in graph neural networks. In: Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, New York, NY, USA, KDD \u201921, pp 1894\u20131903","DOI":"10.1145\/3447548.3467451"},{"key":"1743_CR34","unstructured":"Xu K, Hu W, Leskovec J et\u00a0al (2019) How powerful are graph neural networks? In: Proceedings of the International Conference on Learning Representations"},{"key":"1743_CR35","doi-asserted-by":"crossref","unstructured":"Yao Q, Chen X, Kwok JT et al (2020) Efficient neural interaction function search for collaborative filtering. New York, NY, USA, pp 1660\u20131670","DOI":"10.1145\/3366423.3380237"},{"key":"1743_CR36","doi-asserted-by":"crossref","unstructured":"Yu W, Lin X, Ge J et\u00a0al (2020) Semi-supervised collaborative filtering by text-enhanced domain adaptation. In: Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, New York, NY, USA, KDD \u201920, pp 2136\u20132144","DOI":"10.1145\/3394486.3403264"},{"key":"1743_CR37","doi-asserted-by":"crossref","unstructured":"Zhang H, Shen F, Liu W et\u00a0al (2016) Discrete collaborative filtering. In: Proceedings of the 39th International ACM SIGIR Conference on Research and Development in Information Retrieval, New York, NY, USA, SIGIR \u201916, pp 325\u2013334","DOI":"10.1145\/2911451.2911502"},{"issue":"5","key":"1743_CR38","doi-asserted-by":"publisher","first-page":"102629","DOI":"10.1016\/j.ipm.2021.102629","volume":"58","author":"J Zheng","year":"2021","unstructured":"Zheng J, Li Q, Liao J (2021) Heterogeneous type-specific entity representation learning for recommendations in e-commerce network. Inf Process Manag 58(5):102629","journal-title":"Inf Process Manag"},{"issue":"15","key":"1743_CR39","doi-asserted-by":"publisher","first-page":"107402","DOI":"10.1016\/j.knosys.2021.107402","volume":"230","author":"J Zheng","year":"2021","unstructured":"Zheng J, Li Q, Liao J et al (2021) Explainable link prediction based on multi-granularity relation-embedded representation. Knowl-Based Syst 230(15):107402","journal-title":"Knowl-Based Syst"},{"key":"1743_CR40","doi-asserted-by":"crossref","unstructured":"Zheng L, Lu CT, Jiang F et\u00a0al (2018) Spectral collaborative filtering. In: Proceedings of the 12th ACM Conference on Recommender Systems, New York, NY, USA, RecSys \u201918, pp 311\u2013319","DOI":"10.1145\/3240323.3240343"},{"key":"1743_CR41","doi-asserted-by":"publisher","first-page":"135","DOI":"10.1016\/j.ins.2018.02.053","volume":"444","author":"Y Zhou","year":"2018","unstructured":"Zhou Y, Huang C, Hu Q et al (2018) Personalized learning full-path recommendation model based on lstm neural networks. Inf Sci 444:135\u2013152","journal-title":"Inf Sci"},{"key":"1743_CR42","doi-asserted-by":"crossref","unstructured":"Zou L, Xia L, Gu Y et al (2020) Neural interactive collaborative filtering. New York, NY, USA, pp 749\u2013758","DOI":"10.1145\/3397271.3401181"}],"container-title":["International Journal of Machine Learning and Cybernetics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s13042-022-01743-z.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s13042-022-01743-z\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s13042-022-01743-z.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,5,11]],"date-time":"2023-05-11T03:12:46Z","timestamp":1683774766000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s13042-022-01743-z"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,12,23]]},"references-count":42,"journal-issue":{"issue":"6","published-print":{"date-parts":[[2023,6]]}},"alternative-id":["1743"],"URL":"https:\/\/doi.org\/10.1007\/s13042-022-01743-z","relation":{},"ISSN":["1868-8071","1868-808X"],"issn-type":[{"value":"1868-8071","type":"print"},{"value":"1868-808X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,12,23]]},"assertion":[{"value":"9 June 2022","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"5 December 2022","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"23 December 2022","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}