{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,21]],"date-time":"2026-03-21T08:01:21Z","timestamp":1774080081572,"version":"3.50.1"},"reference-count":40,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2026,2,4]],"date-time":"2026-02-04T00:00:00Z","timestamp":1770163200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2026,2,4]],"date-time":"2026-02-04T00:00:00Z","timestamp":1770163200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["User Model User-Adap Inter"],"published-print":{"date-parts":[[2026,3]]},"DOI":"10.1007\/s11257-026-09442-y","type":"journal-article","created":{"date-parts":[[2026,2,4]],"date-time":"2026-02-04T01:37:38Z","timestamp":1770169058000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["A novel heterogeneous hypergraph social network recommendation system"],"prefix":"10.1007","volume":"36","author":[{"given":"Rakshita","family":"Mall","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Maheshwari Prasad","family":"Singh","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2026,2,4]]},"reference":[{"issue":"7","key":"9442_CR1","doi-asserted-by":"publisher","first-page":"9989","DOI":"10.1007\/s11042-021-11837-5","volume":"82","author":"P Bellini","year":"2023","unstructured":"Bellini, P., Palesi, L.A.I., Nesi, P., Pantaleo, G.: Multi clustering recommendation system for fashion retail. Multimed. Tools Appl. 82(7), 9989\u201310016 (2023)","journal-title":"Multimed. Tools Appl."},{"key":"9442_CR2","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2022.108921","volume":"249","author":"Y Chen","year":"2022","unstructured":"Chen, Y., Wang, J., Wu, Z., Lin, Y.: Integrating user-group relationships under interest similarity constraints for social recommendation. Knowl.-Based Syst. 249, 108921 (2022)","journal-title":"Knowl.-Based Syst."},{"key":"9442_CR3","doi-asserted-by":"crossref","unstructured":"Cheng, D., Chen, J., Peng, W., Ye, W., Lv, F., Zhuang, T. & He, X.. Ihgnn: Interactive hypergraph neural network for personalized product search. In Proceedings of the ACM Web Conference 2022\u00a0(pp. 256\u2013265) (2022).","DOI":"10.1145\/3485447.3511954"},{"key":"9442_CR4","doi-asserted-by":"publisher","DOI":"10.1007\/s00521-023-08679-7","author":"S Dawn","year":"2023","unstructured":"Dawn, S., Das, M., Bandyopadhyay, S.: Soura: a user-reliability-aware social recommendation system based on graph neural network. Neural Comput. Appl. (2023). https:\/\/doi.org\/10.1007\/s00521-023-08679-7","journal-title":"Neural Comput. Appl."},{"key":"9442_CR5","doi-asserted-by":"publisher","unstructured":"Feng, S., Li, J., Wang, Y., & Li, X. (2024). Temporal meta-graph learning for dynamic recommendation. Proceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR \u201824), 1441\u20131450. https:\/\/doi.org\/10.1145\/3626772.3657921","DOI":"10.1145\/3626772.3657921"},{"key":"9442_CR6","doi-asserted-by":"crossref","unstructured":"La Gatta, V., Moscato, V., Pennone, M., Postiglione, M., & Sperl\u00ed, G.. Music recommendation via hypergraph embedding.\u202fIEEE transactions on neural networks and learning systems (2022).","DOI":"10.1109\/TNNLS.2022.3146968"},{"key":"9442_CR7","doi-asserted-by":"crossref","unstructured":"Gharahighehi, A., Vens, C., & Pliakos, K.. An Ensemble Hypergraph Learning Framework for Recommendation. In Discovery Science: 24th International Conference, DS 2021, Halifax, NS, Canada, October 11\u201313, 2021, Proceedings 24, (pp. 295\u2013304). Springer International Publishing (2021).","DOI":"10.1007\/978-3-030-88942-5_23"},{"issue":"4","key":"9442_CR8","doi-asserted-by":"publisher","first-page":"2776","DOI":"10.1109\/TII.2020.2986316","volume":"17","author":"Z Guo","year":"2020","unstructured":"Guo, Z., Wang, H.: A deep graph neural network-based mechanism for social recommendations. IEEE Trans. Ind. Inform. 17(4), 2776\u20132783 (2020)","journal-title":"IEEE Trans. Ind. Inform."},{"key":"9442_CR9","doi-asserted-by":"publisher","first-page":"263","DOI":"10.1016\/j.ins.2020.06.041","volume":"540","author":"X Guo","year":"2020","unstructured":"Guo, X., Lin, W., Li, Y., Liu, Z., Yang, L., Zhao, S., Zhu, Z.: DKEN: deep knowledge-enhanced network for recommender systems. Inf. Sci. 540, 263\u2013277 (2020)","journal-title":"Inf. Sci."},{"issue":"4","key":"9442_CR10","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/2827872","volume":"5","author":"FM Harper","year":"2015","unstructured":"Harper, F.M., Konstan, J.A.: The movielens datasets: history and context. ACM Trans. Interact. Intell. Syst. 5(4), 1\u201319 (2015)","journal-title":"ACM Trans. Interact. Intell. Syst."},{"key":"9442_CR11","doi-asserted-by":"crossref","unstructured":"He, R., & McAuley, J.. Ups and downs: Modeling the visual evolution of fashion trends with one-class collaborative filtering. In Proceedings of the 25th International Conference on World Wide Web (pp. 507\u2013517) (2016).","DOI":"10.1145\/2872427.2883037"},{"key":"9442_CR12","doi-asserted-by":"crossref","unstructured":"He, X., Deng, K., Wang, X., Li, Y., Zhang, Y., & Wang, M.. 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) (2020).","DOI":"10.1145\/3397271.3401063"},{"key":"9442_CR13","doi-asserted-by":"publisher","DOI":"10.1109\/access.2023.3280629","author":"T Hong","year":"2023","unstructured":"Hong, T., Ibrahim, N.F.: SoLGR: social enhancement group recommendation via light graph convolution networks. IEEE Access (2023). https:\/\/doi.org\/10.1109\/access.2023.3280629","journal-title":"IEEE Access"},{"key":"9442_CR14","doi-asserted-by":"crossref","unstructured":"Ji, S., Feng, Y., Ji, R., Zhao, X., Tang, W., Gao, Y.. Dual channel hypergraph collaborative filtering. In Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining (pp. 2020\u20132029) (2020).","DOI":"10.1145\/3394486.3403253"},{"key":"9442_CR15","doi-asserted-by":"publisher","first-page":"71","DOI":"10.1016\/j.neucom.2021.03.076","volume":"449","author":"Y Jiang","year":"2021","unstructured":"Jiang, Y., Ma, H., Liu, Y., Li, Z., Chang, L.: Enhancing social recommendation via two-level graph attentional networks. Neurocomputing 449, 71\u201384 (2021)","journal-title":"Neurocomputing"},{"key":"9442_CR16","doi-asserted-by":"crossref","unstructured":"Jin, B., Gao, C., He, X., Jin, D., & Li, Y. Multi-behavior recommendation with graph convolutional networks. In Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval (pp. 659\u2013668) (2020).","DOI":"10.1145\/3397271.3401072"},{"issue":"3","key":"9442_CR17","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3613964","volume":"3","author":"B Khan","year":"2025","unstructured":"Khan, B., Wu, J., Yang, J., Ma, X.: Heterogeneous hypergraph neural network for social recommendation using attention network. ACM Trans. Recommend. Syst. 3(3), 1\u201322 (2025)","journal-title":"ACM Trans. Recommend. Syst."},{"key":"9442_CR18","first-page":"48765","volume":"36","author":"Y Li","year":"2023","unstructured":"Li, Y., Chen, X., Zhang, M., Ma, W., Liu, Y., Ma, S.: Adaptive heterogeneous graph contrastive learning for recommendation. Adv. Neural Inf. Process. Syst. 36, 48765\u201348778 (2023)","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"9442_CR19","doi-asserted-by":"crossref","unstructured":"Qiu, R., Huang, Z., Yin, H., & Wang, Z. Contrastive learning for representation degeneration problem in sequential recommendation. In Proceedings of the Fifteenth ACM International Conference on Web Search and Data Mining (pp. 813\u2013823) (2022).","DOI":"10.1145\/3488560.3498433"},{"key":"9442_CR20","unstructured":"Rendle, S., Freudenthaler, C., Gantner, Z., & Schmidt-Thieme, L. (2012). BPR: Bayesian personalized ranking from implicit feedback. arXiv preprint arXiv:1205.2618."},{"key":"9442_CR21","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2020.113992","volume":"164","author":"L Sang","year":"2021","unstructured":"Sang, L., Xu, M., Qian, S., Wu, X.: Knowledge graph enhanced neural collaborative recommendation. Expert Syst. Appl. 164, 113992 (2021)","journal-title":"Expert Syst. Appl."},{"issue":"1","key":"9442_CR22","doi-asserted-by":"publisher","DOI":"10.1016\/j.asej.2023.102263","volume":"15","author":"Z Shokrzadeh","year":"2024","unstructured":"Shokrzadeh, Z., Feizi-Derakhshi, M.R., Balafar, M.A., Mohasefi, J.B.: Knowledge graph-based recommendation system enhanced by neural collaborative filtering and knowledge graph embedding. Ain Shams Eng. J. 15(1), 102263 (2024)","journal-title":"Ain Shams Eng. J."},{"key":"9442_CR23","doi-asserted-by":"publisher","unstructured":"Sun, R., Zhang, S., He, X., & Chua, T.-S. Cross-domain implicit preference modeling via contrastive learning. Proceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR \u201824), 1245\u20131254 (2024). https:\/\/doi.org\/10.1145\/3626772.3657804","DOI":"10.1145\/3626772.3657804"},{"issue":"5","key":"9442_CR24","doi-asserted-by":"publisher","DOI":"10.1016\/j.ipm.2020.102277","volume":"57","author":"Z Tao","year":"2020","unstructured":"Tao, Z., Wei, Y., Wang, X., He, X., Huang, X., Chua, T.S.: Mgat: multimodal graph attention network for recommendation. Inf. Process. Manage. 57(5), 102277 (2020)","journal-title":"Inf. Process. Manage."},{"key":"9442_CR25","doi-asserted-by":"publisher","first-page":"87639","DOI":"10.1109\/ACCESS.2022.3199364","volume":"10","author":"Y Wang","year":"2022","unstructured":"Wang, Y., Zhao, Q.: Multi-order hypergraph convolutional neural network for dynamic social recommendation system. IEEE Access 10, 87639\u201387649 (2022)","journal-title":"IEEE Access"},{"key":"9442_CR26","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2022.117552","volume":"204","author":"Z Wang","year":"2022","unstructured":"Wang, Z., Chen, J., Rosas, F.E., Zhu, T.: A hypergraph-based framework for personalized recommendations via user preference and dynamics clustering. Expert Syst. Appl. 204, 117552 (2022)","journal-title":"Expert Syst. Appl."},{"issue":"6","key":"9442_CR27","doi-asserted-by":"publisher","DOI":"10.3390\/math11061346","volume":"11","author":"L Wang","year":"2023","unstructured":"Wang, L., Mistry, S., Hasan, A.A., Hassan, A.O., Islam, Y., Junior Osei, F.A.: Implementation of a collaborative recommendation system based on multi-clustering. Mathematics 11(6), 1346 (2023a)","journal-title":"Mathematics"},{"key":"9442_CR28","doi-asserted-by":"crossref","unstructured":"Wang, X., He, X., Cao, Y., Liu, M., & Chua, T. S. Kgat: Knowledge graph attention network for recommendation. In Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining (pp. 950\u2013958) (2019).","DOI":"10.1145\/3292500.3330989"},{"key":"9442_CR29","doi-asserted-by":"crossref","unstructured":"Wang, Y., Tang, S., Lei, Y., Song, W., Wang, S., & Zhang, M. Disenhan: Disentangled heterogeneous graph attention network for recommendation. In\u202fProceedings of the 29th ACM International Conference on Information & Knowledge Management\u202f(pp. 1605\u20131614) (2020).","DOI":"10.1145\/3340531.3411996"},{"key":"9442_CR30","doi-asserted-by":"publisher","unstructured":"Wang, X., Yin, H., Wang, Q., Chen, T., Huang, Z., & Cui, B.. Graph learning for recommender systems: A survey. Proceedings of the 31st International Joint Conference on Artificial Intelligence (IJCAI \u201822), 5166\u20135173 (2022). https:\/\/doi.org\/10.24963\/ijcai.2022\/722","DOI":"10.24963\/ijcai.2022\/722"},{"key":"9442_CR31","doi-asserted-by":"publisher","unstructured":"Wang, H., Zhang, Q., & Zhou, J.. Implicit user-item interaction modeling for recommendation. Proceedings of the 29th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining (KDD \u201823), 1834\u20131844 (2023). https:\/\/doi.org\/10.1145\/3580305.3599471","DOI":"10.1145\/3580305.3599471"},{"issue":"1","key":"9442_CR32","doi-asserted-by":"publisher","DOI":"10.1186\/s40537-021-00425-x","volume":"8","author":"T Widiyaningtyas","year":"2021","unstructured":"Widiyaningtyas, T., Hidayah, I., Adji, T.B.: User profile correlation-based similarity (UPCSim) algorithm in movie recommendation system. J. Big Data 8(1), 52 (2021)","journal-title":"J. Big Data"},{"issue":"1","key":"9442_CR33","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3523225","volume":"1","author":"S Wu","year":"2022","unstructured":"Wu, S., Sun, F., Zhang, W., Xie, X., Cui, B.: Graph neural networks in recommender systems: a survey. ACM Trans. Recomm. Syst. 1(1), 1\u201345 (2022). https:\/\/doi.org\/10.1145\/3523225","journal-title":"ACM Trans. Recomm. Syst."},{"key":"9442_CR34","doi-asserted-by":"crossref","unstructured":"Wu, Q., Zhang, H., Gao, X., He, P., Weng, P., Gao, H., & Chen, G.. Dual graph attention networks for deep latent representation of multifaceted social effects in recommender systems. In\u202fthe World Wide Web Conference\u202f(pp. 2091\u20132102) (2019).","DOI":"10.1145\/3308558.3313442"},{"key":"9442_CR35","doi-asserted-by":"crossref","unstructured":"Xia, L., Huang, C., Xu, Y., Zhao, J., Yin, D., & Huang, J.. Hypergraph contrastive collaborative filtering. In Proceedings of the 45th International ACM SIGIR Conference on Research and Development In Information Retrieval (pp. 70\u201379) (2022).","DOI":"10.1145\/3477495.3532058"},{"key":"9442_CR36","doi-asserted-by":"publisher","DOI":"10.1145\/3587693","author":"M Yan","year":"2023","unstructured":"Yan, M., Cheng, Z., Gao, C., Sun, J., Liu, F., Sun, F., Li, H.: Cascading residual graph convolutional network for multi-behavior recommendation. ACM Trans. Inf. Syst. (2023). https:\/\/doi.org\/10.1145\/3587693","journal-title":"ACM Trans. Inf. Syst."},{"key":"9442_CR37","doi-asserted-by":"crossref","unstructured":"Yu, J., Yin, H., Li, J., Wang, Q., Hung, N. Q. V., & Zhang, X.. Self-supervised multi-channel hypergraph convolutional network for social recommendation. In Proceedings of the Web Conference 2021 (pp. 413\u2013424) (2021).","DOI":"10.1145\/3442381.3449844"},{"issue":"5","key":"9442_CR38","doi-asserted-by":"publisher","first-page":"4521","DOI":"10.1109\/TKDE.2022.3148982","volume":"35","author":"L Zhao","year":"2023","unstructured":"Zhao, L., Akoglu, L., Eliassi-Rad, T.: Robust social recommendation with noisy links. IEEE Trans. Knowl. Data Eng. 35(5), 4521\u20134534 (2023). https:\/\/doi.org\/10.1109\/TKDE.2022.3148982","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"9442_CR39","doi-asserted-by":"publisher","unstructured":"Zhou, H., Wu, J., Yu, J., & Liu, Q.. Dynamic hypergraph attention networks for recommendation. Proceedings of the Web Conference (WWW \u201824), 2819\u20132830 (2024). https:\/\/doi.org\/10.1145\/3589334.3645531","DOI":"10.1145\/3589334.3645531"},{"key":"9442_CR40","unstructured":"Zhu, Z., Gao, C., Chen, X., Li, N., Jin, D., & Li, Y.. Inhomogeneous social recommendation with hypergraph convolutional networks.\u202farXiv preprint arXiv:2111.03344. (2021)"}],"container-title":["User Modeling and User-Adapted Interaction"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11257-026-09442-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11257-026-09442-y","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11257-026-09442-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,3,21]],"date-time":"2026-03-21T06:30:05Z","timestamp":1774074605000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11257-026-09442-y"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,2,4]]},"references-count":40,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2026,3]]}},"alternative-id":["9442"],"URL":"https:\/\/doi.org\/10.1007\/s11257-026-09442-y","relation":{},"ISSN":["0924-1868","1573-1391"],"issn-type":[{"value":"0924-1868","type":"print"},{"value":"1573-1391","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,2,4]]},"assertion":[{"value":"22 February 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"12 January 2026","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"4 February 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 assert that they have no competing interests, either financial or personal, that could potentially impact the results or interpretation of this work.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}},{"value":"The research conducted in this study did not involve human or animal subjects.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical approval"}}],"article-number":"6"}}