{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,24]],"date-time":"2026-07-24T10:06:04Z","timestamp":1784887564324,"version":"3.55.0"},"reference-count":38,"publisher":"Springer Science and Business Media LLC","issue":"2","license":[{"start":{"date-parts":[[2026,1,11]],"date-time":"2026-01-11T00:00:00Z","timestamp":1768089600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2026,1,11]],"date-time":"2026-01-11T00:00:00Z","timestamp":1768089600000},"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":["J Supercomput"],"DOI":"10.1007\/s11227-025-08187-6","type":"journal-article","created":{"date-parts":[[2026,1,11]],"date-time":"2026-01-11T07:41:31Z","timestamp":1768117291000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Cross-domain recommendation framework for enhanced personalization through contrastive learning"],"prefix":"10.1007","volume":"82","author":[{"given":"Rabia","family":"Khan","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Naima","family":"Iltaf","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Rabia","family":"Latif","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Nor Shahida","family":"Mohd Jamail","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,1,11]]},"reference":[{"key":"8187_CR1","doi-asserted-by":"publisher","first-page":"1506","DOI":"10.1016\/j.ins.2022.07.132","volume":"609","author":"Y Djenouri","year":"2022","unstructured":"Djenouri Y, Belhadi A, Srivastava G, Lin C-W (2022) Deep learning based hashtag recommendation system for multimedia data. Inf Sci 609:1506\u20131517. https:\/\/doi.org\/10.1016\/j.ins.2022.07.132","journal-title":"Inf Sci"},{"key":"8187_CR2","doi-asserted-by":"publisher","unstructured":"Sajjad M, Ramzan F, Khan MUG, Rehman A, Kolivand M, Fati SM, Bahaj SA (2021) Deep convolutional generative adversarial network for Alzheimer\u2019s disease classification using positron emission tomography (PET) and synthetic data augmentation, in WILEY, Jul-2021. https:\/\/doi.org\/10.1002\/jemt.23861","DOI":"10.1002\/jemt.23861"},{"key":"8187_CR3","unstructured":"Cui Q, Wei T, Zhang Y, Zhang Q (2020) HeroGRAPH: a heterogeneous graph framework for multi-target cross-domain recommendation. In: ORSUM@ RecSys"},{"key":"8187_CR4","unstructured":"Li Y, Liu K, Satapathy R, Wang S, Cambria E (2023) Recent developments in recommender systems: a survey. arXiv:2306.12680"},{"issue":"2","key":"8187_CR5","doi-asserted-by":"publisher","first-page":"1553","DOI":"10.32604\/cmc.2021.016348","volume":"69","author":"M Al-Ghobari","year":"2021","unstructured":"Al-Ghobari M, Muneer A, Mohamed Fati S (2021) Location-aware personalized traveler recommender system (LAPTA) using collaborative filtering KNN. Comput Mater Continua 69(2):1553\u20131570","journal-title":"Comput Mater Continua"},{"key":"8187_CR6","first-page":"4503","volume":"35","author":"X Xia","year":"2021","unstructured":"Xia X, Yin H, Junliang Yu, Wang Q, Cui L, Zhang X (2021) Self-supervised hypergraph convolutional networks for session based recommendation. Proc AAAI Conf Artif Intell 35:4503\u20134511","journal-title":"Proc AAAI Conf Artif Intell"},{"key":"8187_CR7","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"},{"key":"8187_CR8","doi-asserted-by":"publisher","first-page":"738","DOI":"10.1016\/j.ins.2023.01.051","volume":"625","author":"M Elahi","year":"2023","unstructured":"Elahi M, Kholgh DK, Kiarostami MS, Oussalah M, Saghari S (2023) Hybrid recommendation by incorporating the sentiment of product reviews. Inf Sci 625:738. https:\/\/doi.org\/10.1016\/j.ins.2023.01.051","journal-title":"Inf Sci"},{"key":"8187_CR9","doi-asserted-by":"crossref","unstructured":"Yang W,Jian Y, Wang Y,Lu S,Shen L, Wang B,Tang H, Zhang L (2024) Not all embeddings are created equal: towards robust cross-domain recommendation via contrastive learning. In: Proceedings of the ACM Web Conference 2024, Association for Computing Machinery, New York, NY, USA, pp 3195\u20133206. https:\/\/doi.org\/10.1145\/3589334.3645357","DOI":"10.1145\/3589334.3645357"},{"issue":"3","key":"8187_CR10","doi-asserted-by":"publisher","first-page":"736","DOI":"10.1109\/TSC.2018.2821686","volume":"14","author":"D Ryu","year":"2021","unstructured":"Ryu D, Lee K, Baik J (2021) Location-based web service QoS prediction via preference propagation to address cold start problem. IEEE Trans Serv Comput 14(3):736\u2013746","journal-title":"IEEE Trans Serv Comput"},{"key":"8187_CR11","doi-asserted-by":"publisher","unstructured":"Li C, Xie Y, Yu C, Hu B, Li Z, Shu G, Qie X, Niu D (2023) One for all, all for one: learning and transferring user embeddings for cross-domain recommendation. In: Proceedings of the 16th ACM International Conference on Web Search and Data Mining (WSDM), pp 366\u2013374. https:\/\/doi.org\/10.1145\/3539597.3570379","DOI":"10.1145\/3539597.3570379"},{"key":"8187_CR12","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2022.108359","volume":"242","author":"L Shi","year":"2022","unstructured":"Shi L, Wen W, Wenxin H, Zhou J, Chen J, Zheng W, He L (2022) DualGCN: an aspect-aware dual graph convolutional network for review-based recommender. Knowl-Based Syst 242:108359. https:\/\/doi.org\/10.1016\/j.knosys.2022.108359","journal-title":"Knowl-Based Syst"},{"key":"8187_CR13","doi-asserted-by":"publisher","first-page":"2969","DOI":"10.1007\/s11280-023-01173-z","volume":"26","author":"T Kumari","year":"2023","unstructured":"Kumari T, Gupta B, Sharma R et al (2023) Empowering reciprocal recommender system using contextual bandits and argumentation based explanations. World Wide Web 26:2969\u20133000. https:\/\/doi.org\/10.1007\/s11280-023-01173-z","journal-title":"World Wide Web"},{"key":"8187_CR14","doi-asserted-by":"publisher","unstructured":"Huai Z, Yang Y, Zhang M, Zhang Z, Li Y, Wu W (2023) M2GNN: metapath and multi-interest aggregated graph neural network for tag-based cross-domain recommendation. In: Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR \u201923), USA, pp 1468\u20131477. https:\/\/doi.org\/10.1145\/3539618.3591720","DOI":"10.1145\/3539618.3591720"},{"key":"8187_CR15","doi-asserted-by":"publisher","DOI":"10.1007\/s11042-023-14424-y","volume-title":"A survey on traditional and deep learning copy move forgery detection (CMFD) techniques","author":"MA Elaskily","year":"2023","unstructured":"Elaskily MA, Dessouky MM, Faragallah OS, Sedik A (2023) A survey on traditional and deep learning copy move forgery detection (CMFD) techniques. Springer. https:\/\/doi.org\/10.1007\/s11042-023-14424-y"},{"issue":"1","key":"8187_CR16","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.is.2012.03.001","volume":"38","author":"G Gayatree","year":"2013","unstructured":"Gayatree G, Kakodkar Y, Marian A (2013) Improving the quality of predictions using textual information in online user reviews. Inf. Syst. 38(1):1\u201315","journal-title":"Inf. Syst."},{"key":"8187_CR17","doi-asserted-by":"crossref","unstructured":"Diao Q, Qiu M, Wu C-Y,Smola AJ, Jiang J, Wang C (2014) Jointly modeling aspects, ratings and sentiments for movie recommendation (KDD)","DOI":"10.1145\/2623330.2623758"},{"key":"8187_CR18","doi-asserted-by":"crossref","unstructured":"Zhang Y, Lai G, Zhang M, Zhang Y, Liu Y, Ma S (2014) Explicit factor models for explainable recommendation based on phrase-level sentiment analysis. In: Proceedings of the 37th International ACM SIGIR Conference on Research & Development in Information Retrieval, pp 83\u201392","DOI":"10.1145\/2600428.2609579"},{"issue":"2","key":"8187_CR19","doi-asserted-by":"publisher","first-page":"1171","DOI":"10.1109\/TKDE.2021.3104873","volume":"35","author":"F Zhu","year":"2023","unstructured":"Zhu F, Wang Y, Zhou J, Chen C, Li L, Liu G (2023) A unified framework for cross-domain and cross-system recommendations. IEEE Trans Knowl Data Eng 35(2):1171\u20131184. https:\/\/doi.org\/10.1109\/TKDE.2021.3104873","journal-title":"IEEE Trans Knowl Data Eng"},{"key":"8187_CR20","unstructured":"Tabassum M, Lalitha M (2016) Opinion word expansion and target extraction through double propagation"},{"key":"8187_CR21","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2019.112871","volume":"140","author":"A Da-u","year":"2020","unstructured":"Da-u A, Salim N, Rabiu I, Osman A (2020) Weighted aspect-based opinion mining using deep learning for recommender system. Expert Syst Appl 140:112871","journal-title":"Expert Syst Appl"},{"key":"8187_CR22","doi-asserted-by":"crossref","unstructured":"Wu C, Wu F, Liu J, Huang Y, Xie X (2019) ARP: aspect-aware neural review rating prediction. In: Proceedings of the 28th ACM International Conference on Information and Knowledge Management, 2019, pp 2169\u20132172","DOI":"10.1145\/3357384.3358086"},{"key":"8187_CR23","doi-asserted-by":"crossref","unstructured":"Cheng Z,Ding Y,Zhu L,Kankanhalli M (2018) Aspect-aware latent factor model: Rating prediction with ratings and reviews, in Proceedings of the 2018 world wide web conference, pp. 639-648","DOI":"10.1145\/3178876.3186145"},{"key":"8187_CR24","doi-asserted-by":"publisher","unstructured":"Zhao Q, Wang Q (2024) Contrastive learning for extracting transferable user profiles in cross-domain recommendation system. In: International Joint Conference on Neural Networks (IJCNN), Yokohama, Japan, pp 1\u20138. https:\/\/doi.org\/10.1109\/IJCNN60899.2024.10650410","DOI":"10.1109\/IJCNN60899.2024.10650410"},{"key":"8187_CR25","doi-asserted-by":"crossref","unstructured":"Man T, Shen H, Jin X, Cheng X (2017) Cross-domain recommendation: an embedding and mapping approach. In Proceedings of the 26th International Joint Conference on Artificial Intelligence. AAAI Press, pp 2464\u20132470(2017)","DOI":"10.24963\/ijcai.2017\/343"},{"key":"8187_CR26","doi-asserted-by":"publisher","first-page":"1395","DOI":"10.1007\/s10994-019-05801-6","volume":"108","author":"R Mariappan","year":"2019","unstructured":"Mariappan R, Rajan V (2019) Deep collective matrix factorization for augmented multi-view learning. Mach Learn 108:1395\u20131420. https:\/\/doi.org\/10.1007\/s10994-019-05801-6","journal-title":"Mach Learn"},{"key":"8187_CR27","doi-asserted-by":"publisher","unstructured":"Guangneng H, Zhang Yu, Yang Q (2018) CoNet: collaborative cross networks for cross-domain recommendation. In: Proceedings of the 27th ACM International Conference on Information and Knowledge Management (CIKM \u201918). Association for Computing Machinery, New York, NY, USA, pp 667\u2013676. https:\/\/doi.org\/10.1145\/3269206.3271684","DOI":"10.1145\/3269206.3271684"},{"key":"8187_CR28","doi-asserted-by":"publisher","unstructured":"Yang W, Zhang S, Huang Z Cross-level graph contrastive learning for community value prediction. https:\/\/doi.org\/10.1016\/j.neunet.2025.108103. ISSN: 0893-6080","DOI":"10.1016\/j.neunet.2025.108103"},{"key":"8187_CR29","unstructured":"Li Y, Liu K, Satapathy R, Wang S, Cambria E (2023) Recent developments in recommender systems: a survey. arXiv:2306.12680"},{"issue":"12","key":"8187_CR30","doi-asserted-by":"publisher","first-page":"btad734","DOI":"10.1093\/bioinformatics\/btad734","volume":"39","author":"Y Li","year":"2023","unstructured":"Li Y, Guo Z, Gao X, Wang G (2023) MMCL-CDR: enhancing cancer drug response prediction with multi-omics and morphology images contrastive representation learning. Bioinformatics 39(12):btad734. https:\/\/doi.org\/10.1093\/bioinformatics\/btad734","journal-title":"Bioinformatics"},{"key":"8187_CR31","doi-asserted-by":"publisher","unstructured":"Feng Y, Li L, Xiang Y, Qin X (2023) PromptCL: improving event representation via prompt template and contrastive learning. In: Natural Language Processing and Chinese Computing: 12th National CCF Conference, NLPCC 2023, Foshan, China, October 12-15, 2023, Proceedings, Part I. Springer-Verlag, Berlin, Heidelberg, pp 261\u2013272. https:\/\/doi.org\/10.1007\/978-3-031-44693-1-21","DOI":"10.1007\/978-3-031-44693-1-21"},{"key":"8187_CR32","doi-asserted-by":"publisher","DOI":"10.1016\/j.dss.2019.113099","volume":"124","author":"X Sun","year":"2019","unstructured":"Sun X, Han M, Feng J (2019) Helpfulness of online reviews: Examining review informativeness and classification thresholds by search products and experience products. Decis Support Syst 124:113099","journal-title":"Decis Support Syst"},{"key":"8187_CR33","doi-asserted-by":"crossref","unstructured":"Ren X, Tang J, Yin D, Chawla NV, Huang C (2024) A survey of large language models for graphs. In: Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, KDD 2024, Barcelona, Spain, August 25\u201329, 2024, pp 6616\u20136626. ACM","DOI":"10.1145\/3637528.3671460"},{"key":"8187_CR34","doi-asserted-by":"crossref","unstructured":"Zhu Y, Peng Y, Zhang H, Tang J (2021) Personalized transfer of user preferences for cross-domain recommendation. arXiv preprint. arXiv:2110.11154","DOI":"10.1145\/3488560.3498392"},{"key":"8187_CR35","doi-asserted-by":"publisher","DOI":"10.1007\/s00500-022-07178-6","author":"M Gan","year":"2022","unstructured":"Gan M, Ma Y (2022) Knowledge transfer learning from multiple user activities to improve personalized recommendation. Soft Comput. https:\/\/doi.org\/10.1007\/s00500-022-07178-6","journal-title":"Soft Comput"},{"key":"8187_CR36","doi-asserted-by":"publisher","unstructured":"Zhao C, Li C, Fu C (2019) Cross-domain recommendation via preference propagation GraphNet. In: Proceedings of the 28th ACM International Conference on Information and Knowledge Management. Association for Computing Machinery, New York, NY, USA, pp 2165\u20132168. https:\/\/doi.org\/10.1145\/3357384.3358166(2019)","DOI":"10.1145\/3357384.3358166"},{"key":"8187_CR37","doi-asserted-by":"publisher","first-page":"90724","DOI":"10.1109\/ACCESS.2023.3307015","volume":"11","author":"R Khan","year":"2023","unstructured":"Khan R, Iltaf N, Latif R, Jamail N (2023) Cross-domain recommendation based on MetaData using graph convolution networks. IEEE Access 11:90724\u201390738","journal-title":"IEEE Access"},{"key":"8187_CR38","doi-asserted-by":"publisher","unstructured":"Ayub, M, Ghazanfar Ma, Mehmood Z, Saba T, Alharbey R, Munshi Am, Alrige Ma (2019) Modeling user rating preference behaviour to improve the performance of the collaborative filtering based recommender systems, in PUBLIC LIBRARY, Jan-2019. https:\/\/doi.org\/10.1371\/journal.pone.0220129","DOI":"10.1371\/journal.pone.0220129"}],"container-title":["The Journal of Supercomputing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11227-025-08187-6.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11227-025-08187-6","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11227-025-08187-6.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,1,11]],"date-time":"2026-01-11T07:41:33Z","timestamp":1768117293000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11227-025-08187-6"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,1,11]]},"references-count":38,"journal-issue":{"issue":"2","published-online":{"date-parts":[[2026,1]]}},"alternative-id":["8187"],"URL":"https:\/\/doi.org\/10.1007\/s11227-025-08187-6","relation":{},"ISSN":["1573-0484"],"issn-type":[{"value":"1573-0484","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,1,11]]},"assertion":[{"value":"7 August 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"20 December 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"11 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. The authors declare no Conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}},{"value":"Not relevant.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval and consent to participate"}},{"value":"All authors have reviewed the final version of the manuscript and give their full consent for its publication.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for publication"}}],"article-number":"55"}}