{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,11]],"date-time":"2025-12-11T07:40:31Z","timestamp":1765438831009,"version":"3.37.3"},"reference-count":33,"publisher":"Springer Science and Business Media LLC","issue":"4","license":[{"start":{"date-parts":[[2022,11,5]],"date-time":"2022-11-05T00:00:00Z","timestamp":1667606400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2022,11,5]],"date-time":"2022-11-05T00:00:00Z","timestamp":1667606400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"name":"National Social Science Foundation project","award":["17BXW065"],"award-info":[{"award-number":["17BXW065"]}]},{"DOI":"10.13039\/501100012166","name":"National Key R &D Program of China","doi-asserted-by":"crossref","award":["2018******01"],"award-info":[{"award-number":["2018******01"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Neural Process Lett"],"published-print":{"date-parts":[[2023,8]]},"DOI":"10.1007\/s11063-022-11077-0","type":"journal-article","created":{"date-parts":[[2022,11,5]],"date-time":"2022-11-05T11:03:19Z","timestamp":1667646199000},"page":"5013-5029","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["Knowledge Graph Attention Network with Attribute Significance for Personalized Recommendation"],"prefix":"10.1007","volume":"55","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9803-6711","authenticated-orcid":false,"given":"Chenyu","family":"Wang","sequence":"first","affiliation":[]},{"given":"Haiyang","family":"Zhang","sequence":"additional","affiliation":[]},{"given":"Lingxiao","family":"Li","sequence":"additional","affiliation":[]},{"given":"Dun","family":"Li","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2022,11,5]]},"reference":[{"issue":"11","key":"11077_CR1","first-page":"160","volume":"55","author":"L Cheng","year":"2019","unstructured":"Cheng L, Gao MT (2019) Hybrid recommendation algorithm based on time weighted and LDA clustering [J]. Computer Eng Appl 55(11):160\u2013166","journal-title":"Computer Eng Appl"},{"key":"11077_CR2","doi-asserted-by":"publisher","unstructured":"Jamali M, Ester M (2010) A matrix factorization technique with trust propagation for recommendation in social networks. Proceedings of the Fourth ACM Conference on Recommender Systems, 135\u2013142. https:\/\/doi.org\/10.1145\/1864708.1864736","DOI":"10.1145\/1864708.1864736"},{"key":"11077_CR3","doi-asserted-by":"publisher","unstructured":"Wang HW, Zhang FZ, Hou M et al (2018) Shine: Signed heterogeneous information network embedding for sentiment link prediction. Proceedings of the Eleventh ACM International Conference on Web Search and Data Mining, 592\u2013600. https:\/\/doi.org\/10.1145\/3159652.3159666","DOI":"10.1145\/3159652.3159666"},{"key":"11077_CR4","doi-asserted-by":"publisher","unstructured":"Bansal T, Belanger D, Mccallum A (2016) Ask the GRU: Multi-Task Learning for Deep Text Recommendations. Proceedings of the 10th ACM Conference on Recommender Systems, 107-114. https:\/\/doi.org\/10.1145\/2959100.2959180","DOI":"10.1145\/2959100.2959180"},{"key":"11077_CR5","doi-asserted-by":"publisher","unstructured":"Sharma A, Cosley D (2013) Do social explanations work? Studying and modeling the effects of social explanations in recommender systems. Proceedings of the 22nd international conference on World Wide Web, 1133-1144. https:\/\/doi.org\/10.1145\/2488388.2488487","DOI":"10.1145\/2488388.2488487"},{"issue":"2","key":"11077_CR6","doi-asserted-by":"publisher","first-page":"207","DOI":"10.11992\/tis.201805001","volume":"14","author":"L Chang","year":"2019","unstructured":"Chang L, Zhang WT, Gu TL et al (2019) Review of recommendation systems based on knowledge graph. CAAI Trans Intell Syst 14(2):207\u2013216. https:\/\/doi.org\/10.11992\/tis.201805001","journal-title":"CAAI Trans Intell Syst"},{"key":"11077_CR7","doi-asserted-by":"publisher","unstructured":"Yu X, Ren X, Sun YZ, et al. (2014) Personalized entity recommendation: A heterogeneous information network approach. Proceedings of the 7th ACM International Conference on Web Search and Data Mining, 283-292. https:\/\/doi.org\/10.1145\/2556195.2556259","DOI":"10.1145\/2556195.2556259"},{"key":"11077_CR8","doi-asserted-by":"publisher","unstructured":"Hu BB, Shi C, Zhao WX, et al. (2018) Leveraging meta-path based context for top-n recommendation with a neural co-attention model. Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery And Data Mining, 1531-1540. https:\/\/doi.org\/10.1145\/3219819.3219965","DOI":"10.1145\/3219819.3219965"},{"key":"11077_CR9","doi-asserted-by":"publisher","unstructured":"Zhao H, Yao QM, Li JD, et al. (2017) Meta-graph based recommendation fusion over heterogeneous information networks. Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 635-644. https:\/\/doi.org\/10.1145\/3097983.3098063","DOI":"10.1145\/3097983.3098063"},{"key":"11077_CR10","doi-asserted-by":"publisher","unstructured":"Wang X, Wang DX, Xu CR et al (2019) Explainable reasoning over knowledge graphs for recommendation. Proceedings of the AAAI Conference on Artificial Intelligence. 33(01):5329\u20135336. https:\/\/doi.org\/10.1609\/aaai.v33i01.33015329","DOI":"10.1609\/aaai.v33i01.33015329"},{"key":"11077_CR11","doi-asserted-by":"publisher","unstructured":"Wang X, Huang TL, Wang DX et al (2021) Learning Intents behind Interactions with Knowledge Graph for Recommendation. Proceedings of the Web Conference. 878\u2013887. https:\/\/doi.org\/10.1145\/3442381.3450133","DOI":"10.1145\/3442381.3450133"},{"issue":"12","key":"11077_CR12","doi-asserted-by":"publisher","first-page":"2724","DOI":"10.1109\/TKDE.2017.2754499","volume":"29","author":"Q Wang","year":"2017","unstructured":"Wang Q, Mao Z, Wang B et al (2017) Knowledge graph embedding: a survey of approaches and applications. IEEE Trans Knowl Data Eng 29(12):2724\u20132743. https:\/\/doi.org\/10.1109\/TKDE.2017.2754499","journal-title":"IEEE Trans Knowl Data Eng"},{"key":"11077_CR13","doi-asserted-by":"publisher","unstructured":"Zhang FZ, Yuan NJ, Lian D, et al (2016) Collaborative knowledge base embedding for recommender systems. Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 353-362. https:\/\/doi.org\/10.1145\/2939672.2939673","DOI":"10.1145\/2939672.2939673"},{"key":"11077_CR14","doi-asserted-by":"publisher","unstructured":"Wang HW, Zhang FZ, Xie X, et al (2018) DKN: Deep knowledge-aware network for news recommendation. Proceedings of the 2018 World Wide Web Conference, 1835-1844. https:\/\/doi.org\/10.1145\/3178876.3186175","DOI":"10.1145\/3178876.3186175"},{"key":"11077_CR15","doi-asserted-by":"publisher","unstructured":"Nathani D, Chauhan J, Sharma C, et al (2019) Learning attention-based embeddings for relation prediction in knowledge graphs. Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics. https:\/\/doi.org\/10.18653\/v1\/P19-1466","DOI":"10.18653\/v1\/P19-1466"},{"key":"11077_CR16","doi-asserted-by":"publisher","unstructured":"Zhao XX, Cheng ZY, Zhu L, et al. (2021) UGRec: Modeling directed and undirected relations for recommendation. Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval, 193-202. https:\/\/doi.org\/10.1145\/3404835.3462835","DOI":"10.1145\/3404835.3462835"},{"key":"11077_CR17","doi-asserted-by":"publisher","unstructured":"Wang HW, Zhang FZ, Wang JL, et al. (2018) RippleNet: Propagating user preferences on the knowledge graph for recommender systems. Proceedings of the 27th ACM International Conference on Information and Knowledge Management, 417-426. https:\/\/doi.org\/10.1145\/3269206.3271739","DOI":"10.1145\/3269206.3271739"},{"key":"11077_CR18","doi-asserted-by":"publisher","unstructured":"Wang Z, Lin GY, Tan HB, et al. (2020) CKAN: Collaborative knowledge-aware attentive network for recommender systems. Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval, 219-228. https:\/\/doi.org\/10.1145\/3397271.3401141","DOI":"10.1145\/3397271.3401141"},{"key":"11077_CR19","unstructured":"Hamilton WL, Ying R, Leskovec J (2017) Inductive representation learning on large graphs. Proceedings of the 31st International Conference on Neural Information Processing Systems, 1025-1035"},{"key":"11077_CR20","first-page":"58","volume":"23","author":"J Wang","year":"2019","unstructured":"Wang J (2019) Graph neural network analysis. Modern Computer 23:58\u201362","journal-title":"Modern Computer"},{"key":"11077_CR21","doi-asserted-by":"publisher","unstructured":"Berg R, Kipf TN, Welling M (2018) Graph convolutional matrix completion. Proceedings of KDD\u201918 Deep Learning Day, August 2018, London, UK. https:\/\/doi.org\/10.48550\/arXiv.1706.02263","DOI":"10.48550\/arXiv.1706.02263"},{"key":"11077_CR22","doi-asserted-by":"publisher","unstructured":"Wang HW, Zhao M, Xie X et al (2019) Knowledge graph convolutional networks for recommender systems. Proceedings of World Wide Web Conference, New York: ACM Press, 3307\u20133313. https:\/\/doi.org\/10.1145\/3308558.3313417","DOI":"10.1145\/3308558.3313417"},{"issue":"1","key":"11077_CR23","doi-asserted-by":"publisher","first-page":"176","DOI":"10.3778\/j.issn.1673-9418.2103072","volume":"16","author":"X Li","year":"2022","unstructured":"Li X, Yang XY, Yu J et al (2022) Double end knowledge graph convolutional networks for recommender systems. J Front Computer Sci Technol 16(1):176. https:\/\/doi.org\/10.3778\/j.issn.1673-9418.2103072","journal-title":"J Front Computer Sci Technol"},{"key":"11077_CR24","doi-asserted-by":"crossref","unstructured":"Ji GL, He SZ, Xu LH, et al. (2015) Knowledge graph embedding via dynamic mapping matrix. Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), 687-696","DOI":"10.3115\/v1\/P15-1067"},{"key":"11077_CR25","doi-asserted-by":"publisher","unstructured":"Kipf TN, Welling M (2017) Semi-supervised classification with graph convolutional networks. International Conference on Learning Representations (ICLR), 2017. https:\/\/doi.org\/10.48550\/arXiv.1609.02907","DOI":"10.48550\/arXiv.1609.02907"},{"key":"11077_CR26","unstructured":"Maas AL, Hannun AY, Ng AY (2013) Rectifier nonlinearities improve neural network acoustic models. In ICML, Vol. 30. 3"},{"key":"11077_CR27","doi-asserted-by":"publisher","unstructured":"Veli\u010dkovi\u0107 P, Cucurull G, Casanova A, et al. (2018) Graph attention networks. In Proceedings of the 6th International Conferences on Learning Representations, 2018. https:\/\/doi.org\/10.48550\/arXiv.1710.10903","DOI":"10.48550\/arXiv.1710.10903"},{"key":"11077_CR28","unstructured":"Xu K, Li C, Tian Y, et al. (2018) Representation learning on graphs with jumping knowledge networks. International Conference on Machine Learning. PMLR, 5453-5462"},{"key":"11077_CR29","doi-asserted-by":"publisher","unstructured":"Rendle S, Freudenthaler C, Gantner Z, et al. (2009) BPR: Bayesian personalized ranking from implicit feedback. In UAI. 452-461. https:\/\/doi.org\/10.48550\/arXiv.1205.2618","DOI":"10.48550\/arXiv.1205.2618"},{"key":"11077_CR30","doi-asserted-by":"publisher","unstructured":"He R, McAuley J (2016) Ups and downs: Modeling the visual evolution of fashion trends with one-class collaborative filtering. Proceedings of the 25th International Conference on World Wide Web, 507-517. https:\/\/doi.org\/10.1145\/2872427.2883037","DOI":"10.1145\/2872427.2883037"},{"issue":"3","key":"11077_CR31","doi-asserted-by":"publisher","first-page":"154","DOI":"10.1016\/j.websem.2009.07.002","volume":"7","author":"C Bizer","year":"2009","unstructured":"Bizer C, Lehmann J, Kobilarov G et al (2009) DBpedia: a crystallization point for the web of data. Web Seman Sci Serv Agents on the World Wide Web 7(3):154\u2013165. https:\/\/doi.org\/10.1016\/j.websem.2009.07.002","journal-title":"Web Seman Sci Serv Agents on the World Wide Web"},{"key":"11077_CR32","doi-asserted-by":"publisher","unstructured":"He XN, Liao LZ, Zhang HW, et al. (2017) Neural collaborative filtering. Proceedings of the 26th International Conference on World Wide Web, 173-182. https:\/\/doi.org\/10.1145\/3038912.3052569","DOI":"10.1145\/3038912.3052569"},{"key":"11077_CR33","doi-asserted-by":"publisher","unstructured":"Yang JH, Chen CM, Wang CJ, et al. (2018) HOP-rec: high-order proximity for implicit recommendation. Proceedings of the 12th ACM Conference on Recommender Systems, 140-144. https:\/\/doi.org\/10.1145\/3240323.3240381","DOI":"10.1145\/3240323.3240381"}],"container-title":["Neural Processing Letters"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11063-022-11077-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11063-022-11077-0\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11063-022-11077-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,7,31]],"date-time":"2023-07-31T16:54:02Z","timestamp":1690822442000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11063-022-11077-0"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,11,5]]},"references-count":33,"journal-issue":{"issue":"4","published-print":{"date-parts":[[2023,8]]}},"alternative-id":["11077"],"URL":"https:\/\/doi.org\/10.1007\/s11063-022-11077-0","relation":{},"ISSN":["1370-4621","1573-773X"],"issn-type":[{"type":"print","value":"1370-4621"},{"type":"electronic","value":"1573-773X"}],"subject":[],"published":{"date-parts":[[2022,11,5]]},"assertion":[{"value":"18 October 2022","order":1,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"5 November 2022","order":2,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}]}}