{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,2]],"date-time":"2026-04-02T19:26:39Z","timestamp":1775157999749,"version":"3.50.1"},"reference-count":24,"publisher":"Springer Science and Business Media LLC","issue":"2","license":[{"start":{"date-parts":[[2023,9,25]],"date-time":"2023-09-25T00:00:00Z","timestamp":1695600000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,9,25]],"date-time":"2023-09-25T00:00:00Z","timestamp":1695600000000},"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":["72271024"],"award-info":[{"award-number":["72271024"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["J Intell Inf Syst"],"published-print":{"date-parts":[[2024,4]]},"DOI":"10.1007\/s10844-023-00816-x","type":"journal-article","created":{"date-parts":[[2023,9,25]],"date-time":"2023-09-25T10:01:46Z","timestamp":1695636106000},"page":"317-338","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["C-GDN: core features activated graph dual-attention network for personalized recommendation"],"prefix":"10.1007","volume":"62","author":[{"given":"Xiongtao","family":"Zhang","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mingxin","family":"Gan","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2023,9,25]]},"reference":[{"key":"816_CR1","doi-asserted-by":"publisher","unstructured":"Berg, R. v. d., Kipf, T. N., & Welling, M. (2017). Graph convolutional matrix completion. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 1\u20139. https:\/\/doi.org\/10.48550\/arXiv.1706.02263","DOI":"10.48550\/arXiv.1706.02263"},{"key":"816_CR2","doi-asserted-by":"publisher","unstructured":"Deng, X., Liao, G., & Zeng, Y. (2022). Group event recommendation based on a heterogeneous attribute graph considering long-and short-term preferences. Journal of Intelligent Information Systems, pp. 1\u201327. Springer. https:\/\/doi.org\/10.1007\/s10844-022-00758-w","DOI":"10.1007\/s10844-022-00758-w"},{"key":"816_CR3","doi-asserted-by":"publisher","unstructured":"Forestiero, A. (2022). Heuristic recommendation technique in internet of things featuring swarm intelligence approach. Expert Systems with Applications, vol. 187, p. 115904. Elsevier. https:\/\/doi.org\/10.1016\/j.eswa.2021.115904","DOI":"10.1016\/j.eswa.2021.115904"},{"key":"816_CR4","doi-asserted-by":"publisher","unstructured":"Gan, M., & Kwon, O. C. (2022). A knowledge-enhanced contextual bandit approach for personalized recommendation in dynamic domains. Knowledge-Based Systems, vol. 251, p. 109158. Elsevier. https:\/\/doi.org\/10.1016\/j.knosys.2022.109158","DOI":"10.1016\/j.knosys.2022.109158"},{"key":"816_CR5","doi-asserted-by":"publisher","unstructured":"Gan, M., & Ma, Y. (2022). Deepinteract: Multi-view features interactive learning for sequential recommendation. Expert Systems with Applications, vol. 204, p. 117305. Elsevier. https:\/\/doi.org\/10.1016\/j.eswa.2022.117305","DOI":"10.1016\/j.eswa.2022.117305"},{"key":"816_CR6","doi-asserted-by":"publisher","unstructured":"Gan, M., & Zhang, H. (2023). Viga: A variational graph autoencoder model to infer user interest representations for recommendation. Information Sciences, vol. 640, p. 119039. Elsevier. https:\/\/doi.org\/10.1016\/j.ins.2023.119039","DOI":"10.1016\/j.ins.2023.119039"},{"key":"816_CR7","doi-asserted-by":"publisher","unstructured":"Harper, F. M., & Konstan, J. A. (2015). The movielens datasets: History and context. Acm Transactions on Interactive Intelligent Systems, vol. 5, pp. 1\u201319. Acm New York, NY, USA. https:\/\/doi.org\/10.1145\/2827872","DOI":"10.1145\/2827872"},{"key":"816_CR8","doi-asserted-by":"publisher","unstructured":"He, X., Deng, K., Wang, X., & 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. https:\/\/doi.org\/10.1145\/3397271.3401063","DOI":"10.1145\/3397271.3401063"},{"key":"816_CR9","doi-asserted-by":"publisher","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. https:\/\/doi.org\/10.1145\/3038912.3052569","DOI":"10.1145\/3038912.3052569"},{"key":"816_CR10","doi-asserted-by":"publisher","unstructured":"He, Y., Mao, Y., Xie, X., & et\u00a0al. (2022). An improved recommendation based on graph convolutional network. Journal of Intelligent Information Systems, vol. 59, pp. 801\u2013823. Springer. https:\/\/doi.org\/10.1007\/s10844-022-00727-3","DOI":"10.1007\/s10844-022-00727-3"},{"key":"816_CR11","doi-asserted-by":"publisher","unstructured":"Hu, L., Li, C., Shi, C., & et\u00a0al. (2020). Graph neural news recommendation with long-term and short-term interest modeling. Information Processing & Management, vol. 57, p. 102142. Elsevier. https:\/\/doi.org\/10.1016\/j.ipm.2019.102142","DOI":"10.1016\/j.ipm.2019.102142"},{"key":"816_CR12","doi-asserted-by":"publisher","unstructured":"Kang, W. C., Cheng, D. Z., Yao, T., & et\u00a0al. (2021). Learning to embed categorical features without embedding tables for recommendation. In: Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining, pp. 840\u2013850. https:\/\/doi.org\/10.1145\/3447548.3467304","DOI":"10.1145\/3447548.3467304"},{"key":"816_CR13","doi-asserted-by":"publisher","unstructured":"Li, Z., Cui, Z., Wu, S., & et\u00a0al. (2019). Fi-gnn: Modeling feature interactions via graph neural networks for ctr prediction. In: Proceedings of the 28th ACM International Conference on Information and Knowledge Management, pp. 539\u2013548. https:\/\/doi.org\/10.1145\/3357384.3357951","DOI":"10.1145\/3357384.3357951"},{"key":"816_CR14","doi-asserted-by":"publisher","unstructured":"Liu, H., Zheng, C., Li, D., & et\u00a0al. (2022). Multi-perspective social recommendation method with graph representation learning. Neurocomputing, vol. 468, pp. 469\u2013481. Elsevier. https:\/\/doi.org\/10.1016\/j.neucom.2021.10.050","DOI":"10.1016\/j.neucom.2021.10.050"},{"key":"816_CR15","doi-asserted-by":"publisher","unstructured":"Song, Y., Ye, H., Li, M., & et\u00a0al. (2022). Deep multi-graph neural networks with attention fusion for recommendation. Expert Systems with Applications, vol. 191, p. 116240. Elsevier. https:\/\/doi.org\/10.1016\/j.eswa.2021.116240","DOI":"10.1016\/j.eswa.2021.116240"},{"key":"816_CR16","doi-asserted-by":"publisher","unstructured":"Su, Y., Zhang, R. M., Erfani, S., & et\u00a0al. (2021). Neural graph matching based collaborative filtering. In: Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 849\u2013858. https:\/\/doi.org\/10.1145\/3404835.3462833","DOI":"10.1145\/3404835.3462833"},{"key":"816_CR17","doi-asserted-by":"publisher","unstructured":"Tao, Z., Wei, Y., Wang, X., & et\u00a0al. (2020) Mgat: Multimodal graph attention network for recommendation. Information Processing & Management, vol. 57, p. 102277. Elsevier. https:\/\/doi.org\/10.1016\/j.ipm.2020.102277","DOI":"10.1016\/j.ipm.2020.102277"},{"key":"816_CR18","doi-asserted-by":"publisher","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, pp. 165\u2013174. https:\/\/doi.org\/10.1145\/3331184.3331267","DOI":"10.1145\/3331184.3331267"},{"key":"816_CR19","doi-asserted-by":"publisher","unstructured":"Wu, L., He, X., Wang, X., & et\u00a0al. (2022a). A survey on accuracy-oriented neural recommendation: From collaborative filtering to information-rich recommendation. IEEE Transactions on Knowledge and Data Engineering, vol. 35, pp. 4425\u20134445. IEEE. https:\/\/doi.org\/10.1109\/TKDE.2022.3145690","DOI":"10.1109\/TKDE.2022.3145690"},{"key":"816_CR20","doi-asserted-by":"publisher","unstructured":"Wu, X., He, H., Yang, H., & et\u00a0al. (2023). Pda-gnn: propagation-depth-aware graph neural networks for recommendation. World Wide Web, pp.1\u201322. Springer. https:\/\/doi.org\/10.1007\/s11280-023-01200-z","DOI":"10.1007\/s11280-023-01200-z"},{"key":"816_CR21","doi-asserted-by":"publisher","unstructured":"Wu, S., Sun, F., Zhang, W., & et\u00a0al. (2022b). Graph neural networks in recommender systems: a survey. ACM Computing Surveys, vol. 55, pp. 1\u201337. ACM New York, NY. https:\/\/doi.org\/10.1145\/3535101","DOI":"10.1145\/3535101"},{"key":"816_CR22","doi-asserted-by":"publisher","unstructured":"Zhang, C., Xue, S., Li, J., & et\u00a0al. (2023). Multi-aspect enhanced graph neural networks for recommendation. Neural Networks, vol. 157, pp. 90\u2013102. Elsevier. https:\/\/doi.org\/10.1016\/j.neunet.2022.10.001","DOI":"10.1016\/j.neunet.2022.10.001"},{"key":"816_CR23","doi-asserted-by":"publisher","unstructured":"Zhang, T., Zhao, P., Liu, Y., & et\u00a0al. (2019). Feature-level deeper self-attention network for sequential recommendation. In: Proceedings of the 28th International Joint Conference on Artificial Intelligence, pp. 4320\u20134326. https:\/\/doi.org\/10.5555\/3367471.3367642","DOI":"10.5555\/3367471.3367642"},{"key":"816_CR24","doi-asserted-by":"publisher","unstructured":"Zhou, G., Zhu, X., Song, C., & et\u00a0al. (2018). Deep interest network for click-through rate prediction. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data mining, pp. 1059\u20131068. https:\/\/doi.org\/10.1145\/3219819.3219823","DOI":"10.1145\/3219819.3219823"}],"container-title":["Journal of Intelligent Information Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10844-023-00816-x.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10844-023-00816-x\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10844-023-00816-x.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,5,2]],"date-time":"2024-05-02T14:18:31Z","timestamp":1714659511000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10844-023-00816-x"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,9,25]]},"references-count":24,"journal-issue":{"issue":"2","published-print":{"date-parts":[[2024,4]]}},"alternative-id":["816"],"URL":"https:\/\/doi.org\/10.1007\/s10844-023-00816-x","relation":{},"ISSN":["0925-9902","1573-7675"],"issn-type":[{"value":"0925-9902","type":"print"},{"value":"1573-7675","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,9,25]]},"assertion":[{"value":"19 July 2023","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"30 August 2023","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"30 August 2023","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"25 September 2023","order":4,"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"}},{"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":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}]}}