{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,26]],"date-time":"2026-06-26T13:51:06Z","timestamp":1782481866576,"version":"3.54.5"},"reference-count":62,"publisher":"Elsevier BV","issue":"1","license":[{"start":{"date-parts":[[2027,1,1]],"date-time":"2027-01-01T00:00:00Z","timestamp":1798761600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2027,1,1]],"date-time":"2027-01-01T00:00:00Z","timestamp":1798761600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2027,1,1]],"date-time":"2027-01-01T00:00:00Z","timestamp":1798761600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-017"},{"start":{"date-parts":[[2027,1,1]],"date-time":"2027-01-01T00:00:00Z","timestamp":1798761600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2027,1,1]],"date-time":"2027-01-01T00:00:00Z","timestamp":1798761600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-012"},{"start":{"date-parts":[[2027,1,1]],"date-time":"2027-01-01T00:00:00Z","timestamp":1798761600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2027,1,1]],"date-time":"2027-01-01T00:00:00Z","timestamp":1798761600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-004"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62176236"],"award-info":[{"award-number":["62176236"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100004731","name":"Zhejiang Province Natural Science Foundation","doi-asserted-by":"publisher","award":["LZ24F030011"],"award-info":[{"award-number":["LZ24F030011"]}],"id":[{"id":"10.13039\/501100004731","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Information Processing & Management"],"published-print":{"date-parts":[[2027,1]]},"DOI":"10.1016\/j.ipm.2026.104984","type":"journal-article","created":{"date-parts":[[2026,6,17]],"date-time":"2026-06-17T11:31:48Z","timestamp":1781695908000},"page":"104984","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":0,"special_numbering":"PA","title":["DDMCL: Meta-path diffusion denoising and multi-view contrastive learning for recommendation"],"prefix":"10.1016","volume":"64","author":[{"given":"Xilin","family":"Wen","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9437-262X","authenticated-orcid":false,"given":"Xu-Hua","family":"Yang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mingwu","family":"Liu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hui","family":"Liu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhen-Lei","family":"Huang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0004-8398-2923","authenticated-orcid":false,"given":"Gang-Feng","family":"Ma","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yanbo","family":"Zhou","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"key":"10.1016\/j.ipm.2026.104984_b1","first-page":"17981","article-title":"Structured denoising diffusion models in discrete state-spaces","volume":"34","author":"Austin","year":"2021","journal-title":"Advances in Neural Information Processing Systems"},{"key":"10.1016\/j.ipm.2026.104984_b2","series-title":"LightGCL: Simple yet effective graph contrastive learning for recommendation","author":"Cai","year":"2023"},{"key":"10.1016\/j.ipm.2026.104984_b3","doi-asserted-by":"crossref","unstructured":"Chen, M., Huang, C., Xia, L., Wei, W., Xu, Y., & Luo, R. (2023). Heterogeneous graph contrastive learning for recommendation. In Proceedings of the sixteenth ACM international conference on web search and data mining (pp. 544\u2013552).","DOI":"10.1145\/3539597.3570484"},{"key":"10.1016\/j.ipm.2026.104984_b4","doi-asserted-by":"crossref","unstructured":"Chen, J., Zhang, H., He, X., Nie, L., Liu, W., & Chua, T.-S. (2017). Attentive collaborative filtering: Multimedia recommendation with item-and component-level attention. In Proceedings of the 40th international ACM SIGIR conference on research and development in information retrieval (pp. 335\u2013344).","DOI":"10.1145\/3077136.3080797"},{"key":"10.1016\/j.ipm.2026.104984_b5","series-title":"Proceedings of the thirteenth international conference on artificial intelligence and statistics","first-page":"297","article-title":"Noise-contrastive estimation: A new estimation principle for unnormalized statistical models","author":"Gutmann","year":"2010"},{"key":"10.1016\/j.ipm.2026.104984_b6","series-title":"European conference on computer vision","first-page":"333","article-title":"Vfusion3d: Learning scalable 3d generative models from video diffusion models","author":"Han","year":"2024"},{"key":"10.1016\/j.ipm.2026.104984_b7","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":"10.1016\/j.ipm.2026.104984_b8","doi-asserted-by":"crossref","unstructured":"He, X., Liao, L., Zhang, H., Nie, L., Hu, X., & Chua, T.-S. (2017). Neural collaborative filtering. In Proceedings of the 26th international conference on world wide web (pp. 173\u2013182).","DOI":"10.1145\/3038912.3052569"},{"key":"10.1016\/j.ipm.2026.104984_b9","series-title":"Learning deep representations by mutual information estimation and maximization","author":"Hjelm","year":"2018"},{"key":"10.1016\/j.ipm.2026.104984_b10","first-page":"6840","article-title":"Denoising diffusion probabilistic models","volume":"33","author":"Ho","year":"2020","journal-title":"Advances in Neural Information Processing Systems"},{"key":"10.1016\/j.ipm.2026.104984_b11","doi-asserted-by":"crossref","unstructured":"Hu, Z., Dong, Y., Wang, K., & Sun, Y. (2020). Heterogeneous graph transformer. In Proceedings of the web conference 2020 (pp. 2704\u20132710).","DOI":"10.1145\/3366423.3380027"},{"key":"10.1016\/j.ipm.2026.104984_b12","doi-asserted-by":"crossref","DOI":"10.1016\/j.neucom.2024.128645","article-title":"A comprehensive survey on contrastive learning","volume":"610","author":"Hu","year":"2024","journal-title":"Neurocomputing"},{"key":"10.1016\/j.ipm.2026.104984_b13","doi-asserted-by":"crossref","unstructured":"Jiang, Y., Huang, C., & Huang, L. (2023). Adaptive graph contrastive learning for recommendation. In Proceedings of the 29th ACM SIGKDD conference on knowledge discovery and data mining (pp. 4252\u20134261).","DOI":"10.1145\/3580305.3599768"},{"key":"10.1016\/j.ipm.2026.104984_b14","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"},{"key":"10.1016\/j.ipm.2026.104984_b15","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"},{"key":"10.1016\/j.ipm.2026.104984_b16","doi-asserted-by":"crossref","DOI":"10.1016\/j.asoc.2021.107396","article-title":"A fuzzy recommendation system for predicting the customers interests using sentiment analysis and ontology in e-commerce","volume":"108","author":"Karthik","year":"2021","journal-title":"Applied Soft Computing"},{"key":"10.1016\/j.ipm.2026.104984_b17","doi-asserted-by":"crossref","unstructured":"Lee, D., Kang, S., Ju, H., Park, C., & Yu, H. (2021). Bootstrapping user and item representations for one-class collaborative filtering. In Proceedings of the 44th international ACM SIGIR conference on research and development in information retrieval (pp. 317\u2013326).","DOI":"10.1145\/3404835.3462935"},{"key":"10.1016\/j.ipm.2026.104984_b18","article-title":"Hypergraph-enhanced multi-interest learning for multi-behavior sequential recommendation","volume":"255","author":"Li","year":"2024","journal-title":"Expert Systems with Applications"},{"key":"10.1016\/j.ipm.2026.104984_b19","doi-asserted-by":"crossref","unstructured":"Li, X., Wu, Y., Jiang, X., Guo, Z., Gong, M., Cao, H., Liu, Y., Jiang, D., & Sun, X. (2024). Enhancing visual document understanding with contrastive learning in large visual-language models. In Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition (pp. 15546\u201315555).","DOI":"10.1109\/CVPR52733.2024.01472"},{"key":"10.1016\/j.ipm.2026.104984_b20","doi-asserted-by":"crossref","unstructured":"Liao, J., Li, S., Yang, Z., Wu, J., Yuan, Y., Wang, X., & He, X. (2024). Llara: Large language-recommendation assistant. In Proceedings of the 47th international ACM SIGIR conference on research and development in information retrieval (pp. 1785\u20131795).","DOI":"10.1145\/3626772.3657690"},{"issue":"2","key":"10.1016\/j.ipm.2026.104984_b21","doi-asserted-by":"crossref","DOI":"10.1016\/j.ipm.2023.103601","article-title":"A social-aware Gaussian pre-trained model for effective cold-start recommendation","volume":"61","author":"Liu","year":"2024","journal-title":"Information Processing & Management"},{"key":"10.1016\/j.ipm.2026.104984_b22","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"},{"issue":"1","key":"10.1016\/j.ipm.2026.104984_b23","doi-asserted-by":"crossref","first-page":"749","DOI":"10.1007\/s10462-021-10043-x","article-title":"News recommender system: a review of recent progress, challenges, and opportunities","volume":"55","author":"Raza","year":"2022","journal-title":"Artificial Intelligence Review"},{"key":"10.1016\/j.ipm.2026.104984_b24","doi-asserted-by":"crossref","unstructured":"Ren, X., Xia, L., Zhao, J., Yin, D., & Huang, C. (2023). Disentangled contrastive collaborative filtering. In Proceedings of the 46th international ACM SIGIR conference on research and development in information retrieval (pp. 1137\u20131146).","DOI":"10.1145\/3539618.3591665"},{"key":"10.1016\/j.ipm.2026.104984_b25","series-title":"BPR: Bayesian personalized ranking from implicit feedback","author":"Rendle","year":"2012"},{"key":"10.1016\/j.ipm.2026.104984_b26","doi-asserted-by":"crossref","DOI":"10.1109\/TKDE.2025.3536096","article-title":"Intent-guided heterogeneous graph contrastive learning for recommendation","author":"Sang","year":"2025","journal-title":"IEEE Transactions on Knowledge and Data Engineering"},{"key":"10.1016\/j.ipm.2026.104984_b27","doi-asserted-by":"crossref","unstructured":"Sedhain, S., Menon, A. K., Sanner, S., & Xie, L. (2015). Autorec: Autoencoders meet collaborative filtering. In Proceedings of the 24th international conference on world wide web (pp. 111\u2013112).","DOI":"10.1145\/2740908.2742726"},{"issue":"2","key":"10.1016\/j.ipm.2026.104984_b28","doi-asserted-by":"crossref","DOI":"10.1016\/j.ipm.2021.102858","article-title":"Leveraging social influence based on users activity centers for point-of-interest recommendation","volume":"59","author":"Seyedhoseinzadeh","year":"2022","journal-title":"Information Processing & Management"},{"issue":"2","key":"10.1016\/j.ipm.2026.104984_b29","doi-asserted-by":"crossref","first-page":"357","DOI":"10.1109\/TKDE.2018.2833443","article-title":"Heterogeneous information network embedding for recommendation","volume":"31","author":"Shi","year":"2018","journal-title":"IEEE Transactions on Knowledge and Data Engineering"},{"issue":"3","key":"10.1016\/j.ipm.2026.104984_b30","doi-asserted-by":"crossref","first-page":"835","DOI":"10.1007\/s10115-016-0925-0","article-title":"Integrating heterogeneous information via flexible regularization framework for recommendation","volume":"49","author":"Shi","year":"2016","journal-title":"Knowledge and Information Systems"},{"key":"10.1016\/j.ipm.2026.104984_b31","doi-asserted-by":"crossref","unstructured":"Singer, U., Roitman, H., Eshel, Y., Nus, A., Guy, I., Levi, O., Hasson, I., & Kiperwasser, E. (2022). Sequential modeling with multiple attributes for watchlist recommendation in e-commerce. In Proceedings of the fifteenth ACM international conference on web search and data mining (pp. 937\u2013946).","DOI":"10.1145\/3488560.3498453"},{"key":"10.1016\/j.ipm.2026.104984_b32","doi-asserted-by":"crossref","DOI":"10.1109\/TNNLS.2025.3583509","article-title":"Diffcl: A diffusion-based contrastive learning framework with semantic alignment for multimodal recommendations","author":"Song","year":"2025","journal-title":"IEEE Transactions on Neural Networks and Learning Systems"},{"key":"10.1016\/j.ipm.2026.104984_b33","series-title":"Infograph: Unsupervised and semi-supervised graph-level representation learning via mutual information maximization","author":"Sun","year":"2019"},{"key":"10.1016\/j.ipm.2026.104984_b34","doi-asserted-by":"crossref","unstructured":"Sun, Y., Sun, Z., Du, Y., Zhang, J., & Ong, Y. S. (2025). Model-agnostic social network refinement with diffusion models for robust social recommendation. In Proceedings of the ACM on web conference 2025 (pp. 370\u2013378).","DOI":"10.1145\/3696410.3714683"},{"key":"10.1016\/j.ipm.2026.104984_b35","doi-asserted-by":"crossref","unstructured":"Sun, Y., Sun, Z., Sha, X., Zhang, J., & Ong, Y. S. (2023). Disentangling motives behind item consumption and social connection for mutually-enhanced joint prediction. In Proceedings of the 17th ACM conference on recommender systems (pp. 613\u2013624).","DOI":"10.1145\/3604915.3608767"},{"key":"10.1016\/j.ipm.2026.104984_b36","series-title":"Deep graph infomax","author":"Veli\u010dkovi\u0107","year":"2018"},{"key":"10.1016\/j.ipm.2026.104984_b37","doi-asserted-by":"crossref","first-page":"8332","DOI":"10.1109\/TIP.2025.3641303","article-title":"Fine-grained image captioning by ranking diffusion transformer","volume":"34","author":"Wan","year":"2025","journal-title":"IEEE Transactions on Image Processing"},{"key":"10.1016\/j.ipm.2026.104984_b38","doi-asserted-by":"crossref","unstructured":"Wang, X., He, X., Cao, Y., Liu, M., & Chua, T.-S. (2019). Kgat: Knowledge graph attention network for recommendation. In Proceedings of the 25th ACM SIGKDD international conference on knowledge discovery & data mining (pp. 950\u2013958).","DOI":"10.1145\/3292500.3330989"},{"key":"10.1016\/j.ipm.2026.104984_b39","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":"10.1016\/j.ipm.2026.104984_b40","doi-asserted-by":"crossref","unstructured":"Wang, X., Ji, H., Shi, C., Wang, B., Ye, Y., Cui, P., & Yu, P. S. (2019). Heterogeneous graph attention network. In The world wide web conference (pp. 2022\u20132032).","DOI":"10.1145\/3308558.3313562"},{"key":"10.1016\/j.ipm.2026.104984_b41","doi-asserted-by":"crossref","unstructured":"Wang, X., Liu, N., Han, H., & Shi, C. (2021). Self-supervised heterogeneous graph neural network with co-contrastive learning. In Proceedings of the 27th ACM SIGKDD conference on knowledge discovery & data mining (pp. 1726\u20131736).","DOI":"10.1145\/3447548.3467415"},{"key":"10.1016\/j.ipm.2026.104984_b42","doi-asserted-by":"crossref","unstructured":"Wang, Y., Tang, S., Lei, Y., Song, W., Wang, S., & Zhang, M. (2020). Disenhan: Disentangled heterogeneous graph attention network for recommendation. In Proceedings of the 29th ACM international conference on information & knowledge management (pp. 1605\u20131614).","DOI":"10.1145\/3340531.3411996"},{"key":"10.1016\/j.ipm.2026.104984_b43","doi-asserted-by":"crossref","first-page":"32465","DOI":"10.52202\/068431-2352","article-title":"Uncovering the structural fairness in graph contrastive learning","volume":"35","author":"Wang","year":"2022","journal-title":"Advances in Neural Information Processing Systems"},{"key":"10.1016\/j.ipm.2026.104984_b44","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":"10.1016\/j.ipm.2026.104984_b45","doi-asserted-by":"crossref","unstructured":"Wang, T., Zhang, B., Zhang, T., Gu, S., Bao, J., Baltrusaitis, T., Shen, J., Chen, D., Wen, F., Chen, Q., et al. (2023). Rodin: A generative model for sculpting 3d digital avatars using diffusion. In Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition (pp. 4563\u20134573).","DOI":"10.1109\/CVPR52729.2023.00443"},{"key":"10.1016\/j.ipm.2026.104984_b46","doi-asserted-by":"crossref","first-page":"20407","DOI":"10.52202\/068431-1484","article-title":"Contrastive graph structure learning via information bottleneck for recommendation","volume":"35","author":"Wei","year":"2022","journal-title":"Advances in Neural Information Processing Systems"},{"issue":"1","key":"10.1016\/j.ipm.2026.104984_b47","doi-asserted-by":"crossref","DOI":"10.1016\/j.ipm.2025.104303","article-title":"ConDiff: Conditional graph diffusion model for recommendation","volume":"63","author":"Wen","year":"2026","journal-title":"Information Processing & Management"},{"key":"10.1016\/j.ipm.2026.104984_b48","doi-asserted-by":"crossref","unstructured":"Wu, J., Wang, X., Feng, F., He, X., Chen, L., Lian, J., & Xie, X. (2021). Self-supervised graph learning for recommendation. In Proceedings of the 44th international ACM SIGIR conference on research and development in information retrieval (pp. 726\u2013735).","DOI":"10.1145\/3404835.3462862"},{"issue":"1","key":"10.1016\/j.ipm.2026.104984_b49","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3530257","article-title":"Personalized news recommendation: Methods and challenges","volume":"41","author":"Wu","year":"2023","journal-title":"ACM Transactions on Information Systems"},{"key":"10.1016\/j.ipm.2026.104984_b50","doi-asserted-by":"crossref","unstructured":"Xi, Y., Liu, W., Lin, J., Cai, X., Zhu, H., Zhu, J., Chen, B., Tang, R., Zhang, W., & Yu, Y. (2024). Towards open-world recommendation with knowledge augmentation from large language models. In Proceedings of the 18th ACM conference on recommender systems (pp. 12\u201322).","DOI":"10.1145\/3640457.3688104"},{"key":"10.1016\/j.ipm.2026.104984_b51","doi-asserted-by":"crossref","unstructured":"Yang, C., Gong, X., Shi, C., & Yu, P. (2023). A post-training framework for improving heterogeneous graph neural networks. In Proceedings of the ACM web conference 2023 (pp. 251\u2013262).","DOI":"10.1145\/3543507.3583282"},{"key":"10.1016\/j.ipm.2026.104984_b52","doi-asserted-by":"crossref","unstructured":"Yang, Y., Huang, C., Xia, L., & Li, C. (2022). Knowledge graph contrastive learning for recommendation. In Proceedings of the 45th international ACM SIGIR conference on research and development in information retrieval (pp. 1434\u20131443).","DOI":"10.1145\/3477495.3532009"},{"issue":"10","key":"10.1016\/j.ipm.2026.104984_b53","doi-asserted-by":"crossref","first-page":"4854","DOI":"10.1109\/TKDE.2020.3045924","article-title":"Heterogeneous network representation learning: A unified framework with survey and benchmark","volume":"34","author":"Yang","year":"2020","journal-title":"IEEE Transactions on Knowledge and Data Engineering"},{"key":"10.1016\/j.ipm.2026.104984_b54","doi-asserted-by":"crossref","unstructured":"Yao, T., Yi, X., Cheng, D. Z., Yu, F., Chen, T., Menon, A., Hong, L., Chi, E. H., Tjoa, S., Kang, J., et al. (2021). Self-supervised learning for large-scale item recommendations. In Proceedings of the 30th ACM international conference on information & knowledge management (pp. 4321\u20134330).","DOI":"10.1145\/3459637.3481952"},{"key":"10.1016\/j.ipm.2026.104984_b55","first-page":"1","article-title":"Collaborative filtering with entity similarity regularization in heterogeneous information networks","volume":"27","author":"Yu","year":"2013","journal-title":"IJCAI HINA"},{"key":"10.1016\/j.ipm.2026.104984_b56","doi-asserted-by":"crossref","unstructured":"Yu, J., Yin, H., Xia, X., Chen, T., Cui, L., & Nguyen, Q. V. H. (2022). Are graph augmentations necessary? simple graph contrastive learning for recommendation. In Proceedings of the 45th international ACM SIGIR conference on research and development in information retrieval (pp. 1294\u20131303).","DOI":"10.1145\/3477495.3531937"},{"issue":"1","key":"10.1016\/j.ipm.2026.104984_b57","doi-asserted-by":"crossref","first-page":"335","DOI":"10.1109\/TKDE.2023.3282907","article-title":"Self-supervised learning for recommender systems: A survey","volume":"36","author":"Yu","year":"2023","journal-title":"IEEE Transactions on Knowledge and Data Engineering"},{"key":"10.1016\/j.ipm.2026.104984_b58","series-title":"Text-like encoding of collaborative information in large language models for recommendation","author":"Zhang","year":"2024"},{"key":"10.1016\/j.ipm.2026.104984_b59","doi-asserted-by":"crossref","unstructured":"Zhang, Y., Sang, L., & Zhang, Y. (2024). Exploring the individuality and collectivity of intents behind interactions for graph collaborative filtering. In Proceedings of the 47th international ACM SIGIR conference on research and development in information retrieval (pp. 1253\u20131262).","DOI":"10.1145\/3626772.3657738"},{"key":"10.1016\/j.ipm.2026.104984_b60","doi-asserted-by":"crossref","unstructured":"Zhao, J., Wenjie, W., Xu, Y., Sun, T., Feng, F., & Chua, T.-S. (2024). Denoising diffusion recommender model. In Proceedings of the 47th international ACM SIGIR conference on research and development in information retrieval (pp. 1370\u20131379).","DOI":"10.1145\/3626772.3657825"},{"key":"10.1016\/j.ipm.2026.104984_b61","article-title":"JCLRec: Joint diffusion model and dual contrastive learning for sequential recommendation","author":"Zhu","year":"2025","journal-title":"Knowledge-Based Systems"},{"key":"10.1016\/j.ipm.2026.104984_b62","doi-asserted-by":"crossref","unstructured":"Zhu, G., Lu, W., Yuan, C., & Huang, Y. (2023). Adamcl: Adaptive fusion multi-view contrastive learning for collaborative filtering. In Proceedings of the 46th international ACM SIGIR conference on research and development in information retrieval (pp. 1076\u20131085).","DOI":"10.1145\/3539618.3591632"}],"container-title":["Information Processing & Management"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0306457326003754?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0306457326003754?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,6,26]],"date-time":"2026-06-26T13:23:29Z","timestamp":1782480209000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0306457326003754"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2027,1]]},"references-count":62,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2027,1]]}},"alternative-id":["S0306457326003754"],"URL":"https:\/\/doi.org\/10.1016\/j.ipm.2026.104984","relation":{},"ISSN":["0306-4573"],"issn-type":[{"value":"0306-4573","type":"print"}],"subject":[],"published":{"date-parts":[[2027,1]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"DDMCL: Meta-path diffusion denoising and multi-view contrastive learning for recommendation","name":"articletitle","label":"Article Title"},{"value":"Information Processing & Management","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.ipm.2026.104984","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2026 Elsevier Ltd. All rights are reserved, including those for text and data mining, AI training, and similar technologies.","name":"copyright","label":"Copyright"}],"article-number":"104984"}}