{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,23]],"date-time":"2026-04-23T21:19:15Z","timestamp":1776979155842,"version":"3.51.4"},"reference-count":35,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T00:00:00Z","timestamp":1780272000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T00:00:00Z","timestamp":1780272000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T00:00:00Z","timestamp":1780272000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-017"},{"start":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T00:00:00Z","timestamp":1780272000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T00:00:00Z","timestamp":1780272000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-012"},{"start":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T00:00:00Z","timestamp":1780272000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T00:00:00Z","timestamp":1780272000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-004"}],"funder":[{"DOI":"10.13039\/501100002338","name":"Ministry of Education of the People's Republic of China","doi-asserted-by":"publisher","award":["CBDIS202403"],"award-info":[{"award-number":["CBDIS202403"]}],"id":[{"id":"10.13039\/501100002338","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62472059"],"award-info":[{"award-number":["62472059"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012458","name":"Chongqing Basic Science and Advanced Technology Research Program","doi-asserted-by":"publisher","award":["CSTB2024TIAD-STX0027"],"award-info":[{"award-number":["CSTB2024TIAD-STX0027"]}],"id":[{"id":"10.13039\/501100012458","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Neurocomputing"],"published-print":{"date-parts":[[2026,6]]},"DOI":"10.1016\/j.neucom.2026.133379","type":"journal-article","created":{"date-parts":[[2026,3,17]],"date-time":"2026-03-17T06:28:50Z","timestamp":1773728930000},"page":"133379","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":0,"special_numbering":"C","title":["Distillation conditional diffusion with spectral-enhanced hierarchical fusion for multi-behavior recommendation"],"prefix":"10.1016","volume":"681","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8239-7176","authenticated-orcid":false,"given":"Xiaofei","family":"Zhu","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Peng","family":"Shan","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"78","reference":[{"key":"10.1016\/j.neucom.2026.133379_bib0005","first-page":"31:1","article-title":"Disentangled cascaded graph convolution networks for multi-behavior recommendation","author":"Cheng","year":"2024","journal-title":"ACM Trans. Recomm. Syst."},{"key":"10.1016\/j.neucom.2026.133379_bib0010","series-title":"Proceedings of the ACM Web Conference 2023","first-page":"1181","article-title":"Multi-behavior recommendation with cascading graph convolution networks","author":"Cheng","year":"2023"},{"key":"10.1016\/j.neucom.2026.133379_bib0015","series-title":"Proceedings of the AAAI Conference on Artificial Intelligence","first-page":"4225","article-title":"Uniform sequence better: time interval aware data augmentation for sequential recommendation","author":"Dang","year":"2023"},{"key":"10.1016\/j.neucom.2026.133379_bib0020","series-title":"Proceedings of the 27th International Joint Conference on Artificial Intelligence (IJCAI)","first-page":"3343","article-title":"Improving implicit recommender systems with view data","author":"Ding","year":"2018"},{"key":"10.1016\/j.neucom.2026.133379_bib0025","series-title":"Proceedings of the IEEE International Conference on Data Engineering","first-page":"1554","article-title":"Neural multi-task recommendation from multi-behavior data","author":"Gao","year":"2019"},{"key":"10.1016\/j.neucom.2026.133379_bib0030","doi-asserted-by":"crossref","first-page":"202","DOI":"10.1016\/j.knosys.2017.10.005","article-title":"Resolving data sparsity by multi-type auxiliary implicit feedback for recommender systems","author":"Guo","year":"2017","journal-title":"Knowl.-Based Syst."},{"key":"10.1016\/j.neucom.2026.133379_bib0035","series-title":"Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining","first-page":"1984","article-title":"Buying or browsing?: predicting real-time purchasing intent using attention-based deep network with multiple behavior","author":"Guo","year":"2019"},{"key":"10.1016\/j.neucom.2026.133379_bib0040","series-title":"Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval","first-page":"639","article-title":"Lightgcn: simplifying and powering graph convolution network for recommendation","author":"He","year":"2020"},{"key":"10.1016\/j.neucom.2026.133379_bib0045","series-title":"Proceedings of the 26th International World Wide Web Conference","first-page":"173","article-title":"Neural collaborative filtering","author":"He","year":"2017"},{"key":"10.1016\/j.neucom.2026.133379_bib0050","series-title":"Advances in Neural Information Processing Systems","first-page":"6840","article-title":"Denoising diffusion probabilistic models","author":"Ho","year":"2020"},{"key":"10.1016\/j.neucom.2026.133379_bib0055","series-title":"Proceedings of the 17th ACM International Conference on Web Search and Data Mining (WSDM)","first-page":"313","article-title":"Diffkg: knowledge graph diffusion model for recommendation","author":"Jiang","year":"2024"},{"key":"10.1016\/j.neucom.2026.133379_bib0060","series-title":"Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval","first-page":"659","article-title":"Multi-behavior recommendation with graph convolutional networks","author":"Jin","year":"2020"},{"key":"10.1016\/j.neucom.2026.133379_bib0065","doi-asserted-by":"crossref","first-page":"2892","DOI":"10.3390\/diagnostics12112892","article-title":"An Al-Biruni Earth radius optimization-based deep convolutional neural network for classifying monkeypox disease","author":"Khafaga","year":"2022","journal-title":"Diagnostics"},{"key":"10.1016\/j.neucom.2026.133379_bib0070","series-title":"3rd International Conference on Learning Representations (ICLR)","article-title":"Adam: a method for stochastic optimization","author":"Kingma","year":"2015"},{"key":"10.1016\/j.neucom.2026.133379_bib0075","series-title":"5th International Conference on Learning Representations (ICLR)","article-title":"Semi-supervised classification with graph convolutional networks","author":"Kipf","year":"2017"},{"key":"10.1016\/j.neucom.2026.133379_bib0080","series-title":"Proceedings of the 5th ACM International Conference on Web Search and Data Mining (WSDM)","first-page":"173","article-title":"Multi-relational matrix factorization using Bayesian personalized ranking for social network data","author":"Krohn-Grimberghe","year":"2012"},{"key":"10.1016\/j.neucom.2026.133379_bib0085","series-title":"Proceedings of the 33rd ACM International Conference on Information and Knowledge Management (CIKM)","first-page":"1163","article-title":"Mule: multi-grained graph learning for multi-behavior recommendation","author":"Lee","year":"2024"},{"key":"10.1016\/j.neucom.2026.133379_bib0090","author":"Li"},{"key":"10.1016\/j.neucom.2026.133379_bib0095","series-title":"Proceedings of the Eighteenth ACM International Conference on Web Search and Data Mining (WSDM)","first-page":"40","article-title":"Diffgraph: heterogeneous graph diffusion model","author":"Li","year":"2025"},{"key":"10.1016\/j.neucom.2026.133379_bib0100","series-title":"Proceedings of the 33rd ACM International Conference on Information and Knowledge Management (CIKM)","first-page":"1346","article-title":"Recdiff: diffusion model for social recommendation","author":"Li","year":"2024"},{"key":"10.1016\/j.neucom.2026.133379_bib0105","series-title":"Proceedings of the Eighteenth ACM International Conference on Web Search and Data Mining","first-page":"773","article-title":"Spectrum-based modality representation fusion graph convolutional network for multimodal recommendation","author":"Ong","year":"2025"},{"key":"10.1016\/j.neucom.2026.133379_bib0110","series-title":"Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence","first-page":"452","article-title":"Bpr: Bayesian personalized ranking from implicit feedback","author":"Rendle","year":"2009"},{"key":"10.1016\/j.neucom.2026.133379_bib0115","series-title":"Proceedings of the European Semantic Web Conference","first-page":"593","article-title":"Modeling relational data with graph convolutional networks","author":"Schlichtkrull","year":"2018"},{"key":"10.1016\/j.neucom.2026.133379_bib0120","series-title":"Proceedings of the 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining","first-page":"650","article-title":"Relational learning via collective matrix factorization","author":"Singh","year":"2008"},{"key":"10.1016\/j.neucom.2026.133379_bib0125","series-title":"Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining","first-page":"283","article-title":"An empirical study on recommendation with multiple types of feedback","author":"Tang","year":"2016"},{"key":"10.1016\/j.neucom.2026.133379_bib0130","series-title":"Advances in Neural Information Processing Systems 30 (NeurIPS)","first-page":"5998","article-title":"Attention is all you need","author":"Vaswani","year":"2017"},{"key":"10.1016\/j.neucom.2026.133379_bib0135","series-title":"Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval","first-page":"832","article-title":"Diffusion recommender model","author":"Wang","year":"2023"},{"key":"10.1016\/j.neucom.2026.133379_bib0140","series-title":"Proceedings of the IEEE International Conference on Data Engineering","first-page":"1931","article-title":"Multi-behavior enhanced recommendation with cross-interaction collaborative relation modeling","author":"Xia","year":"2021"},{"key":"10.1016\/j.neucom.2026.133379_bib0145","series-title":"Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval","first-page":"2397","article-title":"Multiplex behavioral relation learning for recommendation via memory augmented transformer network","author":"Xia","year":"2020"},{"key":"10.1016\/j.neucom.2026.133379_bib0150","series-title":"Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval","first-page":"757","article-title":"Graph meta network for multi-behavior recommendation","author":"Xia","year":"2021"},{"key":"10.1016\/j.neucom.2026.133379_bib0155","first-page":"10:1","article-title":"Cascading residual graph convolutional network for multi-behavior recommendation","author":"Yan","year":"2023","journal-title":"ACM Trans. Inf. Syst."},{"key":"10.1016\/j.neucom.2026.133379_bib0160","author":"Yan"},{"key":"10.1016\/j.neucom.2026.133379_bib0165","doi-asserted-by":"crossref","DOI":"10.1016\/j.neucom.2025.130952","article-title":"Cmc-gcn: consistent multi-granularity cascading graph convolution network for multi-behavior recommendation","author":"Yin","year":"2025","journal-title":"Neurocomputing"},{"key":"10.1016\/j.neucom.2026.133379_bib0170","series-title":"Proceedings of the 31st ACM SIGKDD Conference on Knowledge Discovery and Data Mining","first-page":"1891","article-title":"Combinatorial optimization perspective based framework for multi-behavior recommendation","author":"Zhai","year":"2025"},{"key":"10.1016\/j.neucom.2026.133379_bib0175","series-title":"Proceedings of the 29th ACM International Conference on Information & Knowledge Management (CIKM)","first-page":"2313","article-title":"Multiplex graph neural networks for multi-behavior recommendation","author":"Zhang","year":"2020"}],"container-title":["Neurocomputing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0925231226007769?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0925231226007769?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,4,23]],"date-time":"2026-04-23T20:29:57Z","timestamp":1776976197000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0925231226007769"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,6]]},"references-count":35,"alternative-id":["S0925231226007769"],"URL":"https:\/\/doi.org\/10.1016\/j.neucom.2026.133379","relation":{},"ISSN":["0925-2312"],"issn-type":[{"value":"0925-2312","type":"print"}],"subject":[],"published":{"date-parts":[[2026,6]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Distillation conditional diffusion with spectral-enhanced hierarchical fusion for multi-behavior recommendation","name":"articletitle","label":"Article Title"},{"value":"Neurocomputing","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.neucom.2026.133379","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2026 Elsevier B.V. All rights are reserved, including those for text and data mining, AI training, and similar technologies.","name":"copyright","label":"Copyright"}],"article-number":"133379"}}