{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,9]],"date-time":"2026-07-09T22:34:15Z","timestamp":1783636455152,"version":"3.55.0"},"reference-count":45,"publisher":"Springer Science and Business Media LLC","issue":"36","license":[{"start":{"date-parts":[[2023,6,14]],"date-time":"2023-06-14T00:00:00Z","timestamp":1686700800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,6,14]],"date-time":"2023-06-14T00:00:00Z","timestamp":1686700800000},"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":["62276155"],"award-info":[{"award-number":["62276155"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Neural Comput & Applic"],"published-print":{"date-parts":[[2023,12]]},"DOI":"10.1007\/s00521-023-08703-w","type":"journal-article","created":{"date-parts":[[2023,6,14]],"date-time":"2023-06-14T15:02:10Z","timestamp":1686754930000},"page":"25107-25120","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["CoGCN: co-occurring item-aware GCN for recommendation"],"prefix":"10.1007","volume":"35","author":[{"given":"Xinxiao","family":"Zhao","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Fan","family":"Liu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hao","family":"Liu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mingzhu","family":"Xu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Haoyu","family":"Tang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xueqing","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5653-8286","authenticated-orcid":false,"given":"Yupeng","family":"Hu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2023,6,14]]},"reference":[{"key":"8703_CR1","unstructured":"Bordes A, Usunier N, Garcia-Duran A (2013) Translating embeddings for modeling multi-relational data. In: Proceedings of the 26th conference and workshop on neural information processing systems, p 2787\u20132795"},{"key":"8703_CR2","doi-asserted-by":"crossref","unstructured":"Cao Y, Wang X, He X, Hu Z, Chua TS (2019) Unifying knowledge graph learning and recommendation: towards a better understanding of user preferences. In: Proceedings of the 28th international conference on world wide web, IW3C2, p 151\u2013161","DOI":"10.1145\/3308558.3313705"},{"key":"8703_CR3","doi-asserted-by":"crossref","unstructured":"Chen T, Yin H, Ye G, Huang Z, Wang Y, Wang M (2020) Try this instead: personalized and interpretable substitute recommendation. In: Proceedings of the 43rd international ACM SIGIR conference on research and development in information retrieval, ACM, p 891\u2013900","DOI":"10.1145\/3397271.3401042"},{"key":"8703_CR4","doi-asserted-by":"crossref","unstructured":"Cheng Z, Ding Y, He X, Zhu L, Song X, Kankanhalli MS (2018) A$$^3$$ncf: an adaptive aspect attention model for rating prediction. In: Proceedings of the 27th international joint conference on artificial intelligence, ijcai.org, p 3748\u20133754","DOI":"10.24963\/ijcai.2018\/521"},{"key":"8703_CR5","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3490477","volume":"40","author":"Z Cheng","year":"2021","unstructured":"Cheng Z, Liu F, Mei S, Guo Y, Zhu L, Nie L (2021) Feature-level attentive ICF for recommendation. ACM Trans Inf Syst 40:1\u201324","journal-title":"ACM Trans Inf Syst"},{"issue":"2","key":"8703_CR6","doi-asserted-by":"publisher","first-page":"1900","DOI":"10.1109\/TNSE.2021.3076485","volume":"8","author":"X Deng","year":"2021","unstructured":"Deng X, Guan P, Hei C, Li F, Liu J, Xiong N (2021) An intelligent resource allocation scheme in energy harvesting cognitive wireless sensor networks. IEEE Trans Netw Sci Eng 8(2):1900\u20131912","journal-title":"IEEE Trans Netw Sci Eng"},{"key":"8703_CR7","doi-asserted-by":"publisher","first-page":"2207","DOI":"10.1109\/JIOT.2021.3091216","volume":"9","author":"X Deng","year":"2021","unstructured":"Deng X, Li J, Guan P, Zhang L (2021) Energy-efficient UAV-aided target tracking systems based on edge computing. IEEE Internet Things J 9:2207\u20132214","journal-title":"IEEE Internet Things J"},{"issue":"1","key":"8703_CR8","doi-asserted-by":"publisher","first-page":"143","DOI":"10.1145\/963770.963776","volume":"1","author":"M Deshpande","year":"2004","unstructured":"Deshpande M, Karypis G (2004) Item-based top-N recommendation algorithms. ACM Trans Inf Syst 1(1):143\u2013177","journal-title":"ACM Trans Inf Syst"},{"issue":"7","key":"8703_CR9","first-page":"2121","volume":"12","author":"J Duchi","year":"2011","unstructured":"Duchi J, Hazan E, Singer Y (2011) Adaptive subgradient methods for online learning and stochastic optimization. J Mach Learn Res 12(7):2121\u20132159","journal-title":"J Mach Learn Res"},{"key":"8703_CR10","doi-asserted-by":"crossref","unstructured":"Fu B, Zhang W, Hu G, Dai X, Huang S, Chen J (2021) Dual side deep context-aware modulation for social recommendation. In: Proceedings of the 2021 world wide web conference on world wide web, ACM, p 2524\u20132534","DOI":"10.1145\/3442381.3449940"},{"key":"8703_CR11","unstructured":"Glorot X, Bengio Y (2010) Understanding the difficulty of training deep feedforward neural networks. In: Proceedings of the 13th international conference on artificial intelligence and statistics, p 249\u2013256"},{"key":"8703_CR12","doi-asserted-by":"crossref","unstructured":"He X, Chen T, Kan MY, Chen X (2015) Trirank: reviewaware explainable rec-ommendation by modeling aspects. In: Proceedings of the 24th ACM international conference on information and knowledge management, ACM, p 1661\u20131670","DOI":"10.1145\/2806416.2806504"},{"key":"8703_CR13","doi-asserted-by":"crossref","unstructured":"He X, Liao L, Zhang H, Nie L, Hu X, Chua TS (2017) Neural collaborative filtering. In: Proceedings of the 26th international conference on world wide web, IW3C2, p 173\u2013182","DOI":"10.1145\/3038912.3052569"},{"key":"8703_CR14","doi-asserted-by":"crossref","unstructured":"He X, Deng K, Wang X, Li Y, Zhang Y, Wang M (2020) Lightgcn: simplifying and powering graph convo-lution network for recommendation. In: Proceedings of the 43rd international ACM SIGIR conference on research and development in information retrieval, ACM, p 639\u2013648","DOI":"10.1145\/3397271.3401063"},{"key":"8703_CR15","doi-asserted-by":"crossref","unstructured":"Hsiehz CK, Yangy L, Cuiy Y, Liny TY, Belongiey S, Estrin D (2017) Collaborative metric learning. In: Proceedings of the 26th international conference on world wide web, IW3C2, p 193\u2013201","DOI":"10.1145\/3038912.3052639"},{"key":"8703_CR16","unstructured":"Hu B, Shi C, Zhao WX, Yu PS (2018) Graph convolutional matrix completion. In: Proceedings of the 24th ACM SIGKDD international conference on knowledge discovery & data mining, ACM"},{"key":"8703_CR17","unstructured":"Kingma DP, Ba JL (2015) Adam: a method for stochastic optimization. In: Proceedings of the 3rd international conference on learning representations"},{"key":"8703_CR18","unstructured":"Kipf TN, Welling M (2017) Semi-supervised classification with graph convolutional networks. In: Proceedings of the 5th international conference on learning representations"},{"key":"8703_CR19","doi-asserted-by":"crossref","unstructured":"Liang D, Altosaar J, Charlin L, Blei DM (2016) Factorization meets the item embedding: regularizing matrix factorization with item co-occurrence. In: Proceedings of the 10th ACM conference on recommender systems, ACM, p 59\u201366","DOI":"10.1145\/2959100.2959182"},{"key":"8703_CR20","doi-asserted-by":"publisher","first-page":"4077","DOI":"10.1109\/TKDE.2020.3040772","volume":"34","author":"F Liu","year":"2020","unstructured":"Liu F, Cheng Z, Zhu L, Liu C, Nie L (2020) A$$^2$$gcn: an attribute-aware attentive gcn model for recommendation. IEEE Trans Knowl Data Eng 34:4077\u20134088","journal-title":"IEEE Trans Knowl Data Eng"},{"key":"8703_CR21","doi-asserted-by":"crossref","unstructured":"Liu F, Cheng Z, Liu Y, Zhu L, Gao Z, Nie L (2021a) Interest-aware message-passing GCN for recommendation. In: Proceedings of the 2021 world wide web conference on world wide web, ACM, p 1296\u20131305","DOI":"10.1145\/3442381.3449986"},{"key":"8703_CR22","doi-asserted-by":"publisher","first-page":"1616","DOI":"10.1109\/TITS.2021.3099368","volume":"23","author":"S Liu","year":"2021","unstructured":"Liu S, Yu J, Deng X, Wan S (2021) Fedcpf: an efficient-communication federated learning approach for vehicular edge computing in 6g communication networks. IEEE Trans Intell Transp Syst 23:1616\u20131629","journal-title":"IEEE Trans Intell Transp Syst"},{"key":"8703_CR23","doi-asserted-by":"crossref","unstructured":"Mao K, Zhu J, Xiao X, Lu B, Wang Z, He X (2021) Ultragcn: ultra simplification of graph convolutional networks for recommendation. In: Proceedings of the 30th ACM international conference on information and knowledge management, ACM, p 1253\u20131261","DOI":"10.1145\/3459637.3482291"},{"key":"8703_CR24","doi-asserted-by":"crossref","unstructured":"McAuley J, Targett C, Shi Q, Hengel AJVD (2015) Image-based recommendations on styles and substitutes. In: Proceedings of the 38th international ACM SIGIR conference on research and development in information retrieval, ACM, p 43\u201352","DOI":"10.1145\/2766462.2767755"},{"key":"8703_CR25","doi-asserted-by":"crossref","unstructured":"Park C, Kim D, Oh J, Yu H (2017) Do \u201calso-viewed\u201d products help user rating prediction? In: Proceedings of the 2017 world wide web conference on world wide web, ACM, p 1113\u20131122","DOI":"10.1145\/3038912.3052581"},{"key":"8703_CR26","doi-asserted-by":"crossref","unstructured":"Rendle S, Gantner Z, Freudenthaler C, Thieme LS (2011) Fast context-aware recommendations with factorization machines. In: Proceedings of the 34nd international ACM SIGIR conference on research and development in information retrieval, ACM, p 635\u2013644","DOI":"10.1145\/2009916.2010002"},{"key":"8703_CR27","doi-asserted-by":"crossref","unstructured":"Sun J, Zhang Y, Ma C, Coates M, Guo H, Tang R, He X (2019) Multi-graph convolution collaborative filtering. In: Proceedings of IEEE international conference on data mining, IEEE","DOI":"10.1109\/ICDM.2019.00165"},{"key":"8703_CR28","doi-asserted-by":"crossref","unstructured":"Wang X, He X, Cao Y, Liu M, Chua T (2019a) KGAT: knowledge graph attention network for recommendation. In: Proceedings of the 25th ACM SIGKDD international conference on knowledge discovery & data mining, ACM, p 950\u2013958","DOI":"10.1145\/3292500.3330989"},{"key":"8703_CR29","doi-asserted-by":"crossref","unstructured":"Wang X, He X, Wang M, Feng F, Chua TS (2019b) Neural graph collaborative filtering. In: Proceedings of the 42nd international ACM SIGIR conference on research and development in information retrieval, ACM, p 165\u2013174","DOI":"10.1145\/3331184.3331267"},{"key":"8703_CR30","doi-asserted-by":"crossref","unstructured":"Wang X, Huang T, Wang D, Yuan Y, Liu Z, He X (2021) Learning intents behind interactions with knowledge graph for recommendation. In: Proceedings of the 2021 world wide web conference on world wide web, ACM, p 878\u2013887","DOI":"10.1145\/3442381.3450133"},{"key":"8703_CR31","doi-asserted-by":"crossref","unstructured":"Wang Z, Zhang J, Feng J, Chen Z (2014) Knowledge graph embedding by translating on hyperplanes. In: Proceedings of the 28th international joint conference on artificial intelligence, AAAI, p 1112\u20131119","DOI":"10.1609\/aaai.v28i1.8870"},{"key":"8703_CR32","doi-asserted-by":"publisher","first-page":"3","DOI":"10.1007\/978-3-031-06981-9_1","volume-title":"The semantic web","author":"B Wu","year":"2022","unstructured":"Wu B, Deng C, Guan B, Wang Y, Kangyang Y (2022) Enhancing sequential recommendation via decoupled knowledge graphs. The semantic web. Springer, Cham, pp 3\u201320"},{"key":"8703_CR33","doi-asserted-by":"crossref","unstructured":"Wu L, Yang Y, Zhang K, Hong R, Fu Y, Wang M (2020) Joint item recommendation and attribute inference: an adaptive graph convolutional network approach. In: Proceedings of the 43rd international ACM SIGIR conference on research and development in information retrieval, ACM, p 679\u2013688","DOI":"10.1145\/3397271.3401144"},{"key":"8703_CR34","doi-asserted-by":"crossref","unstructured":"Xin X, He X, Zhang Y, Zhang Y, Jose J (2019) Relational collaborative filtering: modeling multiple item relations for recommendation. In: Proceedings of the 42nd international ACM SIGIR conference on research and development in information retrieval, ACM, p 125\u2013134","DOI":"10.1145\/3331184.3331188"},{"key":"8703_CR35","doi-asserted-by":"publisher","first-page":"5933","DOI":"10.1109\/TIP.2021.3090521","volume":"30","author":"Y Hu","year":"2021","unstructured":"Hu Y, Nie L, Liu M, Wang K, Wang Y, Hua X (2021) Coarse-to-fine semantic alignment for cross-modal moment localization. IEEE Trans Image Process 30:5933\u20135943","journal-title":"IEEE Trans Image Process"},{"key":"8703_CR36","doi-asserted-by":"publisher","first-page":"25479","DOI":"10.1109\/TITS.2021.3110713","volume":"23","author":"H Tang","year":"2022","unstructured":"Tang H, Zhu J, Wang L, Zheng Q, Zhang T (2022) Multi-level query interaction for temporal language grounding. IEEE Trans Intell Transp Syst 23:25479\u201325488","journal-title":"IEEE Trans Intell Transp Syst"},{"key":"8703_CR37","doi-asserted-by":"publisher","first-page":"1338","DOI":"10.1109\/TMM.2021.3063631","volume":"24","author":"H Tang","year":"2022","unstructured":"Tang H, Zhu J, Liu M, Gao Z, Cheng Z (2022) Frame-wise cross-modal matching for video moment retrieval. IEEE Trans Multimed 24:1338\u20131349","journal-title":"IEEE Trans Multimed"},{"key":"8703_CR38","doi-asserted-by":"publisher","first-page":"2854","DOI":"10.1007\/s10489-021-02479-z","volume":"52","author":"M Xu","year":"2021","unstructured":"Xu M, Fu P, Liu B, Yin H, Li J (2021) A novel dynamic graph evolution network for salient object detection. Appl Intell 52:2854\u20132871","journal-title":"Appl Intell"},{"key":"8703_CR39","doi-asserted-by":"publisher","first-page":"4183","DOI":"10.1109\/TIP.2021.3070200","volume":"30","author":"M Xu","year":"2021","unstructured":"Xu M, Fu P, Liu B, Li J (2021) Multi-stream attention-aware graph convolution network for video salient object detection. IEEE Trans Image Process 30:4183\u20134197","journal-title":"IEEE Trans Image Process"},{"key":"8703_CR40","doi-asserted-by":"publisher","first-page":"4667","DOI":"10.1109\/TIP.2021.3073867","volume":"30","author":"Y Hu","year":"2021","unstructured":"Hu Y, Liu M, Su X, Gao Z, Nie L (2021) Video moment localization via deep cross-modal hashing. IEEE Trans Image Process 30:4667\u20134677","journal-title":"IEEE Trans Image Process"},{"key":"8703_CR41","doi-asserted-by":"publisher","first-page":"3169","DOI":"10.1007\/s00521-020-05179-w","volume":"33","author":"Y Hu","year":"2021","unstructured":"Hu Y, Zhan P, Xu Y, Zhao J, Li Y, Li X (2021) Temporal representation learning for time series classification. Neural Comput Appl 33:3169\u20133182","journal-title":"Neural Comput Appl"},{"key":"8703_CR42","doi-asserted-by":"publisher","DOI":"10.1109\/JIOT.2023.3239413","author":"Y Jin","year":"2023","unstructured":"Jin Y, Wang S, Liu F, Fan H, Hu Y, Li X, Liu S (2023) Deep temporal state perception towards artificial cyber-physical systems. IEEE Internet Things J. https:\/\/doi.org\/10.1109\/JIOT.2023.3239413","journal-title":"IEEE Internet Things J"},{"key":"8703_CR43","unstructured":"Yang B, Yih W, He X, Gao J, Deng L (2015) Embedding entities and relations for learning and inference in knowledge bases. In: Proceedings of the 3rd international conference on learning representations"},{"key":"8703_CR44","unstructured":"Zhang Y, Ai Q, Chen X, Wang P (2018) Learning over knowledge-base embeddings for recommendation. In: Proceedings of the 41st international ACM SIGIR conference on research and development in information retrieval, ACM"},{"key":"8703_CR45","doi-asserted-by":"crossref","unstructured":"Zhao X, Cheng Z, Zhu L, Zheng J, Li X (2021) Ugrec: modeling directed and undirected relations for recommendation. In: Proceedings of the 44nd international ACM SIGIR conference on research and development in information retrieval, ACM, p 193\u2013202","DOI":"10.1145\/3404835.3462835"}],"container-title":["Neural Computing and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-023-08703-w.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00521-023-08703-w\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-023-08703-w.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,11,29]],"date-time":"2023-11-29T15:10:28Z","timestamp":1701270628000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00521-023-08703-w"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,6,14]]},"references-count":45,"journal-issue":{"issue":"36","published-print":{"date-parts":[[2023,12]]}},"alternative-id":["8703"],"URL":"https:\/\/doi.org\/10.1007\/s00521-023-08703-w","relation":{},"ISSN":["0941-0643","1433-3058"],"issn-type":[{"value":"0941-0643","type":"print"},{"value":"1433-3058","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,6,14]]},"assertion":[{"value":"29 October 2022","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"23 May 2023","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"14 June 2023","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"All authors declare that they have no conflicts of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}},{"value":"This article does not contain any studies with human participants or animals performed by any of the authors.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical approval"}},{"value":"Informed consent was obtained from all individual participants included in the study.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Informed consent"}}]}}