{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,30]],"date-time":"2026-03-30T11:04:56Z","timestamp":1774868696667,"version":"3.50.1"},"reference-count":53,"publisher":"Springer Science and Business Media LLC","issue":"2","license":[{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"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":["62306157"],"award-info":[{"award-number":["62306157"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Appl Intell"],"published-print":{"date-parts":[[2026,1]]},"DOI":"10.1007\/s10489-026-07094-4","type":"journal-article","created":{"date-parts":[[2026,1,22]],"date-time":"2026-01-22T14:02:58Z","timestamp":1769090578000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Conditional guided diffusion model in latent space for social recommendation"],"prefix":"10.1007","volume":"56","author":[{"given":"Yijun","family":"Hu","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Rui","family":"Tang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1249-9190","authenticated-orcid":false,"given":"Xian","family":"Mo","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2026,1,22]]},"reference":[{"issue":"16","key":"7094_CR1","doi-asserted-by":"publisher","first-page":"8009","DOI":"10.1007\/s10973-022-11827-1","volume":"148","author":"H Azimy","year":"2023","unstructured":"Azimy H, Azimy N, Meghdadi Isfahani AH, Bagherzadeh SA, Farahnakian M (2023) Analysis of thermal performance and ultrasonic wave power variation on heat transfer of heat exchanger in the presence of nanofluid using the artificial neural network: experimental study and model fitting. J Therm Anal Calorim 148(16):8009\u20138023","journal-title":"J Therm Anal Calorim"},{"key":"7094_CR2","doi-asserted-by":"crossref","unstructured":"Akbari M, Bagherzadeh SA, Dehkordi MHR, Naghsh A, Azimy N, Azimy H (2025) Optimizing thermophysical properties of non-newtonian nano-refrigerants for refrigeration systems using machine learning approaches. Int J Refrig","DOI":"10.1016\/j.ijrefrig.2025.08.016"},{"key":"7094_CR3","doi-asserted-by":"publisher","first-page":"103364","DOI":"10.1016\/j.infrared.2020.103364","volume":"108","author":"Y Yongbin","year":"2020","unstructured":"Yongbin Y, Bagherzadeh SA, Azimy H, Akbari M, Karimipour A (2020) Comparison of the artificial neural network model prediction and the experimental results for cutting region temperature and surface roughness in laser cutting of al6061t6 alloy. Infrared Phys Technol 108:103364","journal-title":"Infrared Phys Technol"},{"key":"7094_CR4","doi-asserted-by":"publisher","first-page":"109407","DOI":"10.1016\/j.optlastec.2023.109407","volume":"163","author":"C Sun","year":"2023","unstructured":"Sun C, Dehkordi MHR, Kholoud MJ, Azimy H, Li Z (2023) Systematic evaluation of pulsed laser parameters effect on temperature distribution in dissimilar laser welding: a numerical simulation and artificial neural network. Opt Laser Technol 163:109407","journal-title":"Opt Laser Technol"},{"issue":"2","key":"7094_CR5","doi-asserted-by":"publisher","first-page":"12365","DOI":"10.1111\/exsy.12365","volume":"36","author":"Y Liu","year":"2019","unstructured":"Liu Y, Yang C, Ma J, Xu W, Hua Z (2019) A social recommendation system for academic collaboration in undergraduate research. Expert Syst 36(2):12365","journal-title":"Expert Syst"},{"issue":"2","key":"7094_CR6","first-page":"32","volume":"33","author":"Y Zheng","year":"2010","unstructured":"Zheng Y, Xie X, Ma W-Y et al (2010) Geolife: A collaborative social networking service among user, location and trajectory. IEEE Data Eng Bull 33(2):32\u201339","journal-title":"IEEE Data Eng Bull"},{"key":"7094_CR7","doi-asserted-by":"crossref","unstructured":"Fan W, Ma Y, Yin D, Wang J, Tang J, Li Q (2019) Deep social collaborative filtering. In: Proceedings of the 13th ACM conference on recommender systems, pp 305\u2013313","DOI":"10.1145\/3298689.3347011"},{"issue":"1","key":"7094_CR8","doi-asserted-by":"publisher","first-page":"464","DOI":"10.1109\/TSMC.2018.2872842","volume":"51","author":"L Wu","year":"2018","unstructured":"Wu L, Sun P, Hong R, Ge Y, Wang M (2018) Collaborative neural social recommendation. IEEE Trans Syst Man Cybern Syst 51(1):464\u2013476","journal-title":"IEEE Trans Syst Man Cybern Syst"},{"key":"7094_CR9","doi-asserted-by":"crossref","unstructured":"Sang L, Liu M, Zhang Y, Huang Y, Zhang Y (2024) Bi-directional transfer graph contrastive learning for social recommendation. IEEE Trans Big Data","DOI":"10.1109\/TBDATA.2024.3387340"},{"key":"7094_CR10","doi-asserted-by":"crossref","unstructured":"Zhang X, Xu S, Lin W, Wang S (2023) Constrained social community recommendation. In: Proceedings of the 29th ACM SIGKDD conference on knowledge discovery and data mining, pp 5586\u20135596","DOI":"10.1145\/3580305.3599793"},{"key":"7094_CR11","doi-asserted-by":"crossref","unstructured":"Wang W, Xu Y, Feng F, Lin X, He X, Chua T-S (2023) Diffusion recommender model. In: Proceedings of the 46th International ACM SIGIR conference on research and development in information retrieval, pp 832\u2013841","DOI":"10.1145\/3539618.3591663"},{"key":"7094_CR12","doi-asserted-by":"crossref","unstructured":"Wu Z, Wang X, Chen H, Li K, Han Y, Sun L, Zhu W (2023) Diff4rec: Sequential recommendation with curriculum-scheduled diffusion augmentation. In: Proceedings of the 31st ACM International conference on multimedia, pp 9329\u20139335","DOI":"10.1145\/3581783.3612709"},{"key":"7094_CR13","doi-asserted-by":"crossref","unstructured":"Li Z, Xia L, Huang C (2024) Recdiff: diffusion model for social recommendation. In: Proceedings of the 33rd ACM International conference on information and knowledge management, pp 1346\u20131355","DOI":"10.1145\/3627673.3679630"},{"issue":"1","key":"7094_CR14","doi-asserted-by":"publisher","first-page":"166","DOI":"10.1109\/TSMC.2020.3015355","volume":"52","author":"D Mumin","year":"2020","unstructured":"Mumin D, Shi L-L, Liu L, Panneerselvam J (2020) Data-driven diffusion recommendation in online social networks for the internet of people. IEEE Trans Syst Man Cybern Syst 52(1):166\u2013178","journal-title":"IEEE Trans Syst Man Cybern Syst"},{"key":"7094_CR15","doi-asserted-by":"crossref","unstructured":"Walker J, Zhong T, Zhang F, Gao Q, Zhou F (2022) Recommendation via collaborative diffusion generative model. In: Proceedings of the 25th International conference on knowledge science, engineering and management, pp 593\u2013605","DOI":"10.1007\/978-3-031-10989-8_47"},{"key":"7094_CR16","first-page":"6840","volume":"33","author":"J Ho","year":"2020","unstructured":"Ho J, Jain A, Abbeel P (2020) Denoising diffusion probabilistic models. Adv Neural Inf Process Syst 33:6840\u20136851","journal-title":"Adv Neural Inf Process Syst"},{"key":"7094_CR17","unstructured":"Liu C, Zhang J, Wang S, Fan W, Li Q (2024) Score-based generative diffusion models for social recommendations. arXiv:2412.15579"},{"key":"7094_CR18","unstructured":"Kipf TN, Welling M (2016) Semi-supervised classification with graph convolutional networks. arXiv:1609.02907"},{"issue":"2","key":"7094_CR19","doi-asserted-by":"publisher","first-page":"217","DOI":"10.1137\/090771806","volume":"53","author":"N Halko","year":"2011","unstructured":"Halko N, Martinsson P-G, Tropp JA (2011) Finding structure with randomness: probabilistic algorithms for constructing approximate matrix decompositions. SIAM Rev 53(2):217\u2013288","journal-title":"SIAM Rev"},{"key":"7094_CR20","doi-asserted-by":"crossref","unstructured":"Wu L, Sun P, Fu Y, Hong R, Wang X, Wang M (2019) A neural influence diffusion model for social recommendation. In: Proceedings of the 42nd International ACM SIGIR conference on research and development in information retrieval, pp 235\u2013244","DOI":"10.1145\/3331184.3331214"},{"key":"7094_CR21","doi-asserted-by":"crossref","unstructured":"Xu F, Lian J, Han Z, Li Y, Xu Y, Xie X (2019) Relation-aware graph convolutional networks for agent-initiated social e-commerce recommendation. In: Proceedings of the 28th ACM International conference on information and knowledge management, pp 529\u2013538","DOI":"10.1145\/3357384.3357924"},{"key":"7094_CR22","doi-asserted-by":"crossref","unstructured":"Huang C, Xu H, Xu Y, Dai P, Xia L, Lu M, Bo L, Xing H, Lai X, Ye Y (2021) Knowledge-aware coupled graph neural network for social recommendation. In: Proceedings of the AAAI conference on artificial intelligence, pp 4115\u20134122","DOI":"10.1609\/aaai.v35i5.16533"},{"key":"7094_CR23","doi-asserted-by":"crossref","unstructured":"Chen C, Zhang M, Liu Y, Ma S (2019) Social attentional memory network: modeling aspect-and friend-level differences in recommendation. In: Proceedings of the Twelfth ACM International conference on web search and data mining, pp 177\u2013185","DOI":"10.1145\/3289600.3290982"},{"key":"7094_CR24","doi-asserted-by":"crossref","unstructured":"Fan W, Ma Y, Li Q, He Y, Zhao E, Tang J, Yin D (2019) Graph neural networks for social recommendation. In: The world wide web conference, pp 417\u2013426","DOI":"10.1145\/3308558.3313488"},{"key":"7094_CR25","doi-asserted-by":"crossref","unstructured":"Yu J, Yin H, Li J, Wang Q, Hung NQV, Zhang X (2021) Self-supervised multi-channel hypergraph convolutional network for social recommendation. In: Proceedings of the web conference 2021, pp 413\u2013424","DOI":"10.1145\/3442381.3449844"},{"key":"7094_CR26","doi-asserted-by":"crossref","unstructured":"Long X, Huang C, Xu Y, Xu H, Dai P, Xia L, Bo L (2021) Social recommendation with self-supervised metagraph informax network. In: Proceedings of the 30th ACM International conference on information & knowledge management, pp 1160\u20131169","DOI":"10.1145\/3459637.3482480"},{"key":"7094_CR27","doi-asserted-by":"crossref","unstructured":"Du J, Ye Z, Yao L, Guo B, Yu Z (2022) Socially-aware dual contrastive learning for cold-start recommendation. In: Proceedings of the 45th International ACM SIGIR conference on research and development in information retrieval, pp 1927\u20131932","DOI":"10.1145\/3477495.3531780"},{"key":"7094_CR28","doi-asserted-by":"crossref","unstructured":"Wu J, Fan W, Chen J, Liu S, Li Q, Tang K (2022) Disentangled contrastive learning for social recommendation. In: Proceedings of the 31st ACM International conference on information & knowledge management, pp 4570\u20134574","DOI":"10.1145\/3511808.3557583"},{"key":"7094_CR29","unstructured":"Billsus D, Pazzani MJ et al (1998) Learning collaborative information filters. In: International conference on machine learning, vol 98, pp 46\u201354"},{"issue":"1","key":"7094_CR30","first-page":"807","volume":"32","author":"W Li","year":"2017","unstructured":"Li W, Qi J, Yu Z, Li D (2017) A social recommendation method based on trust propagation and singular value decomposition. J Intell Fuzzy Syst 32(1):807\u2013816","journal-title":"J Intell Fuzzy Syst"},{"key":"7094_CR31","doi-asserted-by":"crossref","unstructured":"Chen H, Wang Z, Huang F, Huang X, Xu Y, Lin Y, He P, Li Z (2022) Generative adversarial framework for cold-start item recommendation. In: Proceedings of the 45th International ACM SIGIR conference on research and development in information retrieval, pp 2565\u20132571","DOI":"10.1145\/3477495.3531897"},{"key":"7094_CR32","first-page":"22629","volume":"33","author":"B Jin","year":"2020","unstructured":"Jin B, Lian D, Liu Z, Liu Q, Ma J, Xie X, Chen E (2020) Sampling-decomposable generative adversarial recommender. Adv Neural Inf Process Syst 33:22629\u201322639","journal-title":"Adv Neural Inf Process Syst"},{"key":"7094_CR33","doi-asserted-by":"crossref","unstructured":"Wang Z, Ye W, Chen X, Zhang W, Wang Z, Zou L, Liu W (2022) Generative session-based recommendation. In: Proceedings of the ACM Web Conference 2022, pp 2227\u20132235","DOI":"10.1145\/3485447.3512095"},{"key":"7094_CR34","doi-asserted-by":"crossref","unstructured":"Liang D, Krishnan RG, Hoffman MD, Jebara T (2018) Variational autoencoders for collaborative filtering. In: Proceedings of the 2018 world wide web conference, pp 689\u2013698","DOI":"10.1145\/3178876.3186150"},{"key":"7094_CR35","unstructured":"Ma J, Zhou C, Cui P, Yang H, Zhu W (2019) Learning disentangled representations for recommendation. Adv Neural Inf Process Syst 32"},{"key":"7094_CR36","doi-asserted-by":"crossref","unstructured":"Li Z, Xia L, Hua H, Zhang S, Wang S, Huang C (2025) Diffgraph: Heterogeneous graph diffusion model. In: Proceedings of the Eighteenth ACM International conference on web search and data mining, pp. 40\u201349","DOI":"10.1145\/3701551.3703590"},{"key":"7094_CR37","doi-asserted-by":"crossref","unstructured":"Jiang Y, Xia L, Wei W, Luo D, Lin K, Huang C (2024) Diffmm: Multi-modal diffusion model for recommendation. In: Proceedings of the 32nd ACM International conference on multimedia, pp 7591\u20137599","DOI":"10.1145\/3664647.3681498"},{"issue":"3","key":"7094_CR38","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3631116","volume":"42","author":"Z Li","year":"2023","unstructured":"Li Z, Sun A, Li C (2023) Diffurec: A diffusion model for sequential recommendation. ACM Trans Inf Syst 42(3):1\u201328","journal-title":"ACM Trans Inf Syst"},{"key":"7094_CR39","doi-asserted-by":"crossref","unstructured":"Jiang Y, Yang Y, Xia L, Huang C (2024) Diffkg: Knowledge graph diffusion model for recommendation. In: Proceedings of the 17th ACM International conference on web search and data mining, pp 313\u2013321","DOI":"10.1145\/3616855.3635850"},{"issue":"1","key":"7094_CR40","doi-asserted-by":"publisher","first-page":"104303","DOI":"10.1016\/j.ipm.2025.104303","volume":"63","author":"X Wen","year":"2026","unstructured":"Wen X, Yang X-H, Ma G-F (2026) Condiff: Conditional graph diffusion model for recommendation. Inf Process Manag 63(1):104303","journal-title":"Inf Process Manag"},{"key":"7094_CR41","doi-asserted-by":"crossref","unstructured":"Wang J, Yu L, Zhang W, Gong Y, Xu Y, Wang B, Zhang P, Zhang D (2017) Irgan: A minimax game for unifying generative and discriminative information retrieval models. In: Proceedings of the 40th International ACM SIGIR conference on research and development in information retrieval, pp 515\u2013524","DOI":"10.1145\/3077136.3080786"},{"key":"7094_CR42","doi-asserted-by":"crossref","unstructured":"Yu X, Zhang X, Cao Y, Xia M (2019) Vaegan: A collaborative filtering framework based on adversarial variational autoencoders. In: IJCAI, vol 19, p 4206","DOI":"10.24963\/ijcai.2019\/584"},{"key":"7094_CR43","doi-asserted-by":"crossref","unstructured":"Liu F, Cheng Z, Zhu L, Gao Z, Nie L (2021) Interest-aware message-passing gcn for recommendation. In: Proceedings of the web conference 2021, pp. 1296\u20131305","DOI":"10.1145\/3442381.3449986"},{"issue":"11","key":"7094_CR44","doi-asserted-by":"publisher","first-page":"11153","DOI":"10.1109\/TKDE.2022.3231352","volume":"35","author":"Y Wei","year":"2022","unstructured":"Wei Y, Wang X, Nie L, Li S, Wang D, Chua T-S (2022) Causal inference for knowledge graph based recommendation. IEEE Trans Knowl Data Eng 35(11):11153\u201311164","journal-title":"IEEE Trans Knowl Data Eng"},{"key":"7094_CR45","doi-asserted-by":"crossref","unstructured":"He X, Deng K, Wang X, Li Y, Zhang Y, Wang M (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","DOI":"10.1145\/3397271.3401063"},{"key":"7094_CR46","doi-asserted-by":"crossref","unstructured":"Wang X, He X, Wang M, Feng F, Chua T-S (2019) Neural graph collaborative filtering. In: Proceedings of the 42nd International ACM SIGIR conference on research and development in information retrieval, pp 165\u2013174","DOI":"10.1145\/3331184.3331267"},{"key":"7094_CR47","doi-asserted-by":"crossref","unstructured":"Lin Z, Tian C, Hou Y, Zhao WX (2022) Improving graph collaborative filtering with neighborhood-enriched contrastive learning. In: Proceedings of the ACM Web Conference 2022, pp 2320\u20132329","DOI":"10.1145\/3485447.3512104"},{"issue":"8","key":"7094_CR48","doi-asserted-by":"publisher","first-page":"1633","DOI":"10.1109\/TPAMI.2016.2605085","volume":"39","author":"B Yang","year":"2016","unstructured":"Yang B, Lei Y, Liu J, Li W (2016) Social collaborative filtering by trust. IEEE Trans Pattern Anal Mach Intell 39(8):1633\u20131647","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"7094_CR49","doi-asserted-by":"crossref","unstructured":"Quan Y, Ding J, Gao C, Yi L, Jin D, Li Y (2023) Robust preference-guided denoising for graph based social recommendation. In: Proceedings of the ACM Web Conference 2023, pp 1097\u20131108","DOI":"10.1145\/3543507.3583374"},{"key":"7094_CR50","doi-asserted-by":"crossref","unstructured":"Song W, Xiao Z, Wang Y, Charlin L, Zhang M, Tang J (2019) Session-based social recommendation via dynamic graph attention networks. In: Proceedings of the Twelfth ACM International conference on web search and data mining, pp 555\u2013563","DOI":"10.1145\/3289600.3290989"},{"key":"7094_CR51","doi-asserted-by":"crossref","unstructured":"Wang T, Xia L, Huang C (2023) Denoised self-augmented learning for social recommendation. arXiv:2305.12685","DOI":"10.24963\/ijcai.2023\/258"},{"key":"7094_CR52","doi-asserted-by":"publisher","first-page":"113008","DOI":"10.1016\/j.asoc.2025.113008","volume":"174","author":"X Mo","year":"2025","unstructured":"Mo X, Pang J, Zhao Z (2025) Attention-aware graph contrastive learning with topological relationship for recommendation. Appl Soft Comput 174:113008","journal-title":"Appl Soft Comput"},{"issue":"3","key":"7094_CR53","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3722103","volume":"43","author":"C Zhao","year":"2025","unstructured":"Zhao C, Yang E, Liang Y, Zhao J, Guo G, Wang X (2025) Symmetric graph contrastive learning against noisy views for recommendation. ACM Trans Inf Syst 43(3):1\u201328","journal-title":"ACM Trans Inf Syst"}],"container-title":["Applied Intelligence"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10489-026-07094-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10489-026-07094-4","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10489-026-07094-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,3,30]],"date-time":"2026-03-30T10:15:55Z","timestamp":1774865755000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10489-026-07094-4"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,1]]},"references-count":53,"journal-issue":{"issue":"2","published-print":{"date-parts":[[2026,1]]}},"alternative-id":["7094"],"URL":"https:\/\/doi.org\/10.1007\/s10489-026-07094-4","relation":{},"ISSN":["0924-669X","1573-7497"],"issn-type":[{"value":"0924-669X","type":"print"},{"value":"1573-7497","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,1]]},"assertion":[{"value":"8 August 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"3 January 2026","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"22 January 2026","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":"Conflicts of Interest"}},{"value":"Ethical and informed consent for data used.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical and informed consent for data used"}},{"value":"We hereby declare that this research does not involve human or animal studies.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Research involving human participants and\/or animals"}}],"article-number":"54"}}