{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,4]],"date-time":"2026-02-04T06:59:44Z","timestamp":1770188384396,"version":"3.49.0"},"reference-count":28,"publisher":"Springer Science and Business Media LLC","issue":"2","license":[{"start":{"date-parts":[[2026,2,3]],"date-time":"2026-02-03T00:00:00Z","timestamp":1770076800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2026,2,3]],"date-time":"2026-02-03T00:00:00Z","timestamp":1770076800000},"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":["Multimed Tools Appl"],"DOI":"10.1007\/s11042-026-21224-7","type":"journal-article","created":{"date-parts":[[2026,2,3]],"date-time":"2026-02-03T19:11:53Z","timestamp":1770145913000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Multimodal fusion in graph-based recommendation systems"],"prefix":"10.1007","volume":"85","author":[{"ORCID":"https:\/\/orcid.org\/0009-0006-5080-376X","authenticated-orcid":false,"given":"Maha","family":"Sayed","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Wedad","family":"Hussien","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yasmine M.","family":"Afify","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Walaa","family":"Gad","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2026,2,3]]},"reference":[{"issue":"2","key":"21224_CR1","doi-asserted-by":"publisher","first-page":"376","DOI":"10.1007\/s11036-019-01246-2","volume":"25","author":"X Yang","year":"2020","unstructured":"Yang X, Zhou S, Cao M (2020) An approach to alleviate the sparsity problem of hybrid collaborative filtering based recommendations: the product-attribute perspective from user reviews. Mob Netw Appl 25(2):376\u2013390","journal-title":"Mob Netw Appl"},{"issue":"11","key":"21224_CR2","doi-asserted-by":"publisher","first-page":"6165","DOI":"10.3390\/su13116165","volume":"13","author":"J Kim","year":"2021","unstructured":"Kim J, Choi I, Li Q (2021) Customer satisfaction of recommender system: examining accuracy and diversity in several types of recommendation approaches. Sustainability 13(11):6165","journal-title":"Sustainability"},{"issue":"4","key":"21224_CR3","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3447772","volume":"54","author":"A Hogan","year":"2021","unstructured":"Hogan A, Blomqvist E, Cochez M, d\u2019Amato C, Melo GD, Gutierrez C, Zimmermann A (2021) Knowledge graphs. ACM Comput Surv (Csur) 54(4):1\u201337","journal-title":"ACM Comput Surv (Csur)"},{"issue":"9","key":"21224_CR4","first-page":"16","volume":"5","author":"CK Suryadevara","year":"2020","unstructured":"Suryadevara CK (2020) Towards personalized healthcare-an intelligent medication recommendation system. iejrd-International Multidisciplinary J 5(9):16","journal-title":"IEJRD-International Multidisciplinary J"},{"key":"21224_CR5","doi-asserted-by":"publisher","DOI":"10.1016\/j.chb.2019.106168","volume":"104","author":"C De Medio","year":"2020","unstructured":"De Medio C, Limongelli C, Sciarrone F, Temperini M (2020) MoodleREC: a recommendation system for creating courses using the moodle e-learning platform. Comput Hum Behav 104:106168","journal-title":"Comput Hum Behav"},{"key":"21224_CR6","doi-asserted-by":"publisher","unstructured":"Schedl M, Knees P, McFee B, Bogdanov D (2022) Music Recommendation Systems: Techniques, Use Cases, and Challenges. In: Ricci F, Rokach L, Shapira B (eds) Recommender systems handbook. Springer, New York, NY. https:\/\/doi.org\/10.1007\/978-1-0716-2197-4_24","DOI":"10.1007\/978-1-0716-2197-4_24"},{"issue":"1","key":"21224_CR7","doi-asserted-by":"publisher","first-page":"439","DOI":"10.1007\/s40747-020-00212-w","volume":"7","author":"Q Zhang","year":"2021","unstructured":"Zhang Q, Lu J, Jin Y (2021) Artificial intelligence in recommender systems. Complex Intell Syst 7(1):439\u2013457","journal-title":"Complex Intell Syst"},{"issue":"5","key":"21224_CR8","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3407190","volume":"53","author":"Y Deldjoo","year":"2020","unstructured":"Deldjoo Y, Schedl M, Cremonesi P, Pasi G (2020) Recommender systems leveraging multimedia content. ACM Comput Surv (CSUR) 53(5):1\u201338","journal-title":"ACM Comput Surv (CSUR)"},{"issue":"1","key":"21224_CR9","doi-asserted-by":"publisher","DOI":"10.1038\/s41467-021-27137-3","volume":"12","author":"Q Ye","year":"2021","unstructured":"Ye Q, Hsieh CY, Yang Z, Kang Y, Chen J, Cao D, He S, Hou T (2021) A unified drug\u2013target interaction prediction framework based on knowledge graph and recommendation system. Nat Commun 12(1):6775","journal-title":"Nat Commun"},{"issue":"16","key":"21224_CR10","doi-asserted-by":"publisher","DOI":"10.1002\/cpe.6232","volume":"35","author":"L Vuong Nguyen","year":"2023","unstructured":"Vuong Nguyen L, Nguyen TH, Jung JJ, Camacho D (2023) Extending collaborative filtering recommendation using word embedding: a hybrid approach. Concurrency Computat Pract Exper 35(16):e6232","journal-title":"Concurrency Computat Pract Exper"},{"issue":"1","key":"21224_CR11","doi-asserted-by":"publisher","DOI":"10.1155\/2021\/5589285","volume":"2021","author":"L Fu","year":"2021","unstructured":"Fu L, Ma X (2021) An improved recommendation method based on content filtering and collaborative filtering. Complexity 2021(1):5589285","journal-title":"Complexity"},{"key":"21224_CR12","unstructured":"MovieLens Website, [Online]. Available: https:\/\/movielens.org\/"},{"key":"21224_CR13","unstructured":"Goel S (2021) Retrieved from https:\/\/www.kaggle.com\/datasets\/pes12017000148\/food-ingredients-and-recipe-dataset-with-images. Accessed 6 Jul 2024"},{"issue":"1","key":"21224_CR14","doi-asserted-by":"publisher","first-page":"43","DOI":"10.1007\/s10844-021-00650-z","volume":"58","author":"JA Sacenti","year":"2022","unstructured":"Sacenti JA, Fileto R, Willrich R (2022) Knowledge graph summarization impacts on movie recommendations. J Intell Inform Syst 58(1):43\u201366","journal-title":"J Intell Inform Syst"},{"key":"21224_CR15","doi-asserted-by":"crossref","unstructured":"Fan H, Zhong Y, Zeng G, Ge C (2022) Improving recommender system via knowledge graph based exploring user preference. Appl Intell 52(9):10032\u201310044","DOI":"10.1007\/s10489-021-02872-8"},{"key":"21224_CR16","doi-asserted-by":"publisher","DOI":"10.1016\/j.mlwa.2023.100507","volume":"14","author":"G Kaur","year":"2023","unstructured":"Kaur G, Liu F, Chen YPP (2023) A deep learning knowledge graph neural network for recommender systems. Machine Learning with Applications 14:100507","journal-title":"Machine Learning with Applications"},{"key":"21224_CR17","doi-asserted-by":"crossref","unstructured":"Mondal P, Chakder D, Raj S, Saha S, Onoe N (2023) Graph convolutional neural network for multimodal movie recommendation. In Proceedings of the 38th ACM\/SIGAPP Symposium on Applied Computing (pp. 1633\u20131640)","DOI":"10.1145\/3555776.3577853"},{"key":"21224_CR18","doi-asserted-by":"crossref","unstructured":"Yu P, Tan Z, Lu G, Bao BK (2023) Multi-view graph convolutional network for multimedia recommendation. In Proceedings of the 31st ACM International Conference on Multimedia (pp. 6576\u20136585)","DOI":"10.1145\/3581783.3613915"},{"issue":"4","key":"21224_CR19","doi-asserted-by":"publisher","DOI":"10.1007\/s42979-023-01870-6","volume":"4","author":"AFUR Khilji","year":"2023","unstructured":"Khilji AFUR, Sinha U, Singh P, Ali A, Dadure P, Manna R, Pakray P (2023) Multimodal recipe recommendation system using deep learning and rule-based approach. SN Comput Sci 4(4):421","journal-title":"SN Comput Sci"},{"key":"21224_CR20","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2023.121278","volume":"235","author":"F Wang","year":"2024","unstructured":"Wang F, Zhu X, Cheng X, Zhang Y, Li Y (2024) Mmkdgat: multi-modal knowledge graph-aware deep graph attention network for remote sensing image recommendation. Expert Syst Appl 235:121278","journal-title":"Expert Syst Appl"},{"issue":"1","key":"21224_CR21","doi-asserted-by":"publisher","first-page":"231","DOI":"10.29100\/jipi.v9i1.4306","volume":"9","author":"MA Pradana","year":"2024","unstructured":"Pradana MA, Wibowo AT (2024) Movie recommendation system using hybrid filtering with word2vec and restricted Boltzmann machines. JIPI (Jurnal Ilmiah Penelitian Dan Pembelajaran Informatika) 9(1):231\u2013241","journal-title":"JIPI (Jurnal Ilmiah Penelitian Dan Pembelajaran Informatika)"},{"key":"21224_CR22","doi-asserted-by":"crossref","unstructured":"Kumar B, Singh AK, Banerjee P (2023) A deep learning approach for product recommendation using resnet-50 cnn model. In 2023 International Conference on Sustainable Computing and Smart Systems (ICSCSS) (pp. 604\u2013610). IEEE","DOI":"10.1109\/ICSCSS57650.2023.10169441"},{"issue":"26","key":"21224_CR23","doi-asserted-by":"publisher","first-page":"34499","DOI":"10.1007\/s11042-019-08607-9","volume":"80","author":"JW Baek","year":"2021","unstructured":"Baek JW, Chung KY (2021) Multimedia recommendation using Word2Vec-based social relationship mining. Multimedia Tools Appl 80(26):34499\u201334515","journal-title":"Multimedia Tools Appl"},{"issue":"02","key":"21224_CR24","first-page":"1546","volume":"7","author":"M Umadevi","year":"2020","unstructured":"Umadevi M (2020) Document comparison based on tf-idf metric. Int Res J Eng Technol (IRJET) 7(02):1546\u20131550","journal-title":"Int Res J Eng Technol (IRJET)"},{"key":"21224_CR25","unstructured":"Epicurious Website, [Online]. Available: https:\/\/www.epicurious.com\/"},{"key":"21224_CR26","doi-asserted-by":"publisher","first-page":"52508","DOI":"10.1109\/ACCESS.2022.3175317","volume":"10","author":"M Rostami","year":"2022","unstructured":"Rostami M, Oussalah M, Farrahi V (2022) A novel time-aware food recommender-system based on deep learning and graph clustering. IEEE Access 10:52508\u201352524","journal-title":"IEEE Access"},{"issue":"2","key":"21224_CR27","first-page":"494","volume":"16","author":"P Vilakone","year":"2020","unstructured":"Vilakone P, Xinchang K, Park DS (2020) Movie recommendation system based on users\u2019 personal information and movies rated using the method of k-clique and normalized discounted cumulative gain. J Inform Process Syst 16(2):494\u2013507","journal-title":"J Inform Process Syst"},{"key":"21224_CR28","unstructured":"PyPI, [Online] Available: https:\/\/pypi.org\/project\/beautifulsoup4\/"}],"container-title":["Multimedia Tools and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11042-026-21224-7.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11042-026-21224-7","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11042-026-21224-7.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,2,3]],"date-time":"2026-02-03T19:11:54Z","timestamp":1770145914000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11042-026-21224-7"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,2,3]]},"references-count":28,"journal-issue":{"issue":"2","published-online":{"date-parts":[[2026,2]]}},"alternative-id":["21224"],"URL":"https:\/\/doi.org\/10.1007\/s11042-026-21224-7","relation":{},"ISSN":["1573-7721"],"issn-type":[{"value":"1573-7721","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,2,3]]},"assertion":[{"value":"14 November 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"31 October 2025","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"18 November 2025","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"3 February 2026","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"Not applicable.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical approval"}},{"value":"Not applicable.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent to participate"}},{"value":"Not applicable.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent to publish"}},{"value":"This research has no competing interests that could have influenced its content, direction, or outcomes.","order":5,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"122"}}