{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,18]],"date-time":"2026-03-18T12:47:17Z","timestamp":1773838037605,"version":"3.50.1"},"reference-count":48,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2026,3,18]],"date-time":"2026-03-18T00:00:00Z","timestamp":1773792000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62207027"],"award-info":[{"award-number":["62207027"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62177024"],"award-info":[{"award-number":["62177024"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Zhejiang Provincial Philosophy and Social Sciences Planning Item","award":["23NDJC112YB"],"award-info":[{"award-number":["23NDJC112YB"]}]},{"name":"Zhejiang Provincial Education Science Planning Item","award":["2023SCG369"],"award-info":[{"award-number":["2023SCG369"]}]},{"name":"Jinhua Municipal Federation of Social Sciences Item","award":["YB2025112"],"award-info":[{"award-number":["YB2025112"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Information"],"abstract":"<jats:p>Item description information plays a crucial role in helping users understand the basic situation of an item and is also vital auxiliary information in recommendation systems. Traditional methods obtain this data through platform backend data or web scraping techniques, but these data are often static, relatively fixed, and insufficiently descriptive. In recent years, large language models (LLMs) like generative pre-trained transformer (GPT) have become powerful tools in natural language processing, bringing new hope for LLM-based recommendations. However, does the text information generated by large language models help improve recommendation accuracy? How can the information produced by generative artificial intelligence be integrated with existing multi-source heterogeneous information? In this paper, we propose a novel deep hybrid recommendation method for multimodal information integrating content generated by large language models (DML). We first explore the use of large language models to generate detailed descriptive information about movies. Next, we perform a weighted fusion of the generated text information with existing movie category information and user demographic data, among other multi-source heterogeneous information. Finally, we use the fused information to predict movie ratings. The results indicate that the multimodal information deep hybrid recommendation method, which integrates content generated by large language models, provides substantial evidence of superior performance relative to existing baseline models.<\/jats:p>","DOI":"10.3390\/info17030298","type":"journal-article","created":{"date-parts":[[2026,3,18]],"date-time":"2026-03-18T09:10:04Z","timestamp":1773825004000},"page":"298","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["A Deep Hybrid Recommendation Method for Multimodal Information Integrating Content Generated by Large Language Models"],"prefix":"10.3390","volume":"17","author":[{"ORCID":"https:\/\/orcid.org\/0009-0002-9315-5417","authenticated-orcid":false,"given":"Chao","family":"Duan","sequence":"first","affiliation":[{"name":"Zhejiang Provincial Key Laboratory of Intelligent Education Technology and Application, Zhejiang Normal University, Jinhua 321004, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0003-7734-3730","authenticated-orcid":false,"given":"Wenlong","family":"Zhang","sequence":"additional","affiliation":[{"name":"Zhejiang Provincial Key Laboratory of Intelligent Education Technology and Application, Zhejiang Normal University, Jinhua 321004, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhongtao","family":"Yu","sequence":"additional","affiliation":[{"name":"Zhejiang Provincial Key Laboratory of Intelligent Education Technology and Application, Zhejiang Normal University, Jinhua 321004, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Senyao","family":"Li","sequence":"additional","affiliation":[{"name":"Zhejiang Provincial Key Laboratory of Intelligent Education Technology and Application, Zhejiang Normal University, Jinhua 321004, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xuelian","family":"Wan","sequence":"additional","affiliation":[{"name":"Zhejiang Provincial Key Laboratory of Intelligent Education Technology and Application, Zhejiang Normal University, Jinhua 321004, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5041-6093","authenticated-orcid":false,"given":"Qionghao","family":"Huang","sequence":"additional","affiliation":[{"name":"Zhejiang Provincial Key Laboratory of Intelligent Education Technology and Application, Zhejiang Normal University, Jinhua 321004, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2026,3,18]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"112934","DOI":"10.1016\/j.asoc.2025.112934","article-title":"Heterogeneous collaborative filtering contrastive learning for social recommendation","volume":"173","author":"Meng","year":"2025","journal-title":"Appl. Soft Comput."},{"key":"ref_2","unstructured":"Mahmud, R., Wu, Y., Bin Sawad, A., Berkovsky, S., Prasad, M., and Kocaballi, A.B. (December, January 29). Evaluating user experience in conversational recommender systems: A systematic review across classical and LLM-powered approaches. Proceedings of the 37th Australian Conference on Human-Computer Interaction, Sydney, Australia."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"734","DOI":"10.1109\/TKDE.2005.99","article-title":"Toward the next generation of recommender systems: A survey of the state-of-the-art and possible extensions","volume":"17","author":"Adomavicius","year":"2005","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Duan, C., Sun, J., Li, K., and Li, Q. (2021). A dual-attention autoencoder network for efficient recommendation system. Electronics, 10.","DOI":"10.3390\/electronics10131581"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"129539","DOI":"10.1016\/j.neucom.2025.129539","article-title":"TPGRec: Text-enhanced and popularity-smoothing graph collaborative filtering for long-tail item recommendation","volume":"626","author":"Yu","year":"2025","journal-title":"Neurocomputing"},{"key":"ref_6","first-page":"1","article-title":"How can recommender systems benefit from large language models: A survey","volume":"43","author":"Lin","year":"2025","journal-title":"ACM Trans. Inf. Syst."},{"key":"ref_7","unstructured":"Bao, H., Dong, L., Piao, S., and Wei, F. (2021). Beit: Bert pre-training of image transformers. arXiv."},{"key":"ref_8","unstructured":"Hou, M., Wu, L., Liao, Y., Yang, Y., Zhang, Z., Zheng, C., Wu, H., and Hong, R. (2025). A survey on generative recommendation: Data, model, and tasks. arXiv."},{"key":"ref_9","unstructured":"Lopez-Avila, A., and Du, J. (2025). A survey on large language models in multimodal recommender systems. arXiv."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"60","DOI":"10.1007\/s11280-024-01291-2","article-title":"A survey on large language models for recommendation","volume":"27","author":"Wu","year":"2024","journal-title":"World Wide Web"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Acharya, A., Singh, B., and Onoe, N. (2023, January 18\u201322). Llm based generation of item-description for recommendation system. Proceedings of the 17th ACM Conference on Recommender Systems, Singapore.","DOI":"10.1145\/3604915.3610647"},{"key":"ref_12","unstructured":"Jin, B., Zeng, H., Wang, G., Chen, X., Wei, T., Li, R., Wang, Z., Li, Z., Li, Y., and Lu, H. (2023). Language models as semantic indexers. arXiv."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3705728","article-title":"Recranker: Instruction tuning large language model as ranker for top-k recommendation","volume":"43","author":"Luo","year":"2025","journal-title":"ACM Trans. Inf. Syst."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Liu, Y., Zhang, W., Chen, Y., Zhang, Y., Bai, H., Feng, F., Cui, H., Li, Y., and Che, W. (2023). Conversational recommender system and large language model are made for each other in E-commerce pre-sales dialogue. Findings of the Association for Computational Linguistics: EMNLP 2023, Association for Computational Linguistics.","DOI":"10.18653\/v1\/2023.findings-emnlp.643"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Wang, Y., Jiang, Z., Chen, Z., Yang, F., Zhou, Y., Cho, E., Fan, X., Lu, Y., Huang, X., and Yang, Y. (2024). Recmind: Large language model powered agent for recommendation. Findings of the Association for Computational Linguistics: NAACL 2024, Association for Computational Linguistics.","DOI":"10.18653\/v1\/2024.findings-naacl.271"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"He, K., Chen, X., Xie, S., Li, Y., Dollar, P., and Girshick, R. (2022, January 18\u201324). Masked autoencoders are scalable vision learners. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, New Orleans, LA, USA.","DOI":"10.1109\/CVPR52688.2022.01553"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Chatterjee, A., Ghosh, S., and Ghosh, A. (2025, January 19\u201323). Context-aware masking and learnable diffusion-guided patch refinement in transformers via sparse supervision for hyperspectral image classification. Proceedings of the IEEE\/CVF International Conference on Computer Vision, Honolulu, Hawaii.","DOI":"10.1109\/ICCVW69036.2025.00306"},{"key":"ref_18","first-page":"1","article-title":"Deep multimodal data fusion","volume":"56","author":"Zhao","year":"2024","journal-title":"ACM Comput. Surv."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Li, J., Peng, B., and Hsu, Y.Y. (2024, January 20\u201325). Emstremo: Adapting emotional support response with enhanced emotion-strategy integrated selection. Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024), Torino, Italy.","DOI":"10.63317\/4ywd639z4qec"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Duan, C., Zhang, W., Cui, Q., Pei, Y., He, B., and Huang, Q. (2025). Enhancing MOOC recommendation through preference-aware knowledge graph diffusion and temporal sequence modeling. Information, 16.","DOI":"10.3390\/info16121061"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"103207","DOI":"10.1016\/j.ipm.2022.103207","article-title":"A question-guided multi-hop reasoning graph network for visual question answering","volume":"60","author":"Xu","year":"2023","journal-title":"Inf. Process. Manag."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"121601","DOI":"10.1016\/j.eswa.2023.121601","article-title":"Contrastive topic-enhanced network for video captioning","volume":"237","author":"Zeng","year":"2024","journal-title":"Expert Syst. Appl."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Zadeh, A., Chen, M., Poria, S., Cambria, E., and Morency, L.-P. (2017). Tensor fusion network for multimodal sentiment analysis. arXiv.","DOI":"10.18653\/v1\/D17-1115"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Hossain, M.R., Afroze, S., Ekbal, A., Hoque, M.M., and Siddique, N. (2025). MultiModFuseNet: Advancing multimodal text classification for low-resource languages through textual-visual feature fusion. Knowl. Based Syst., 114085.","DOI":"10.1016\/j.knosys.2025.114085"},{"key":"ref_25","unstructured":"Narzary, S., Brahma, B., Mahilary, H., Brahma, M., Som, B., and Nandi, S. (2025). Comparative study of zero-shot cross-lingual transfer for BODO POS and NER tagging using GEMINI 2.0 flash thinking experimental model. arXiv."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Balestri, R. (2025). Gender and content bias in large language models: A case study on Google Gemini 2.0 flash experimental. Front. Artif. Intell., 8.","DOI":"10.3389\/frai.2025.1558696"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"43089","DOI":"10.1007\/s11042-023-15224-0","article-title":"A systematic survey on automated text generation tools and techniques: Application, evaluation, and challenges","volume":"82","author":"Goyal","year":"2023","journal-title":"Multimed. Tools Appl."},{"key":"ref_28","first-page":"5999","article-title":"Attention is all you need","volume":"2017","author":"Vaswani","year":"2017","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/2827872","article-title":"The movielens datasets: History and context","volume":"5","author":"Harper","year":"2015","journal-title":"ACM Trans. Interact. Intell. Syst."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Cheng, H.T., Koc, L., Harmsen, J., Shaked, T., Chandra, T., Aradhye, H., Anderson, G., Corrado, G., Chai, W., and Ispir, M. (2016, January 15). Wide & deep learning for recommender systems. Proceedings of the 1st Workshop on Deep Learning for Recommender Systems, Boston, MA, USA.","DOI":"10.1145\/2988450.2988454"},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"He, X., and Chua, T.S. (2017, January 7\u201311). Neural factorization machines for sparse predictive analytics. Proceedings of the 40th International ACM SIGIR Conference on Research and Development in Information Retrieval, Tokyo, Japan.","DOI":"10.1145\/3077136.3080777"},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Guo, H., Tang, R., Ye, Y., Li, Z., and He, X. (2017). DeepFM: A factorization-machine based neural network for CTR prediction. arXiv.","DOI":"10.24963\/ijcai.2017\/239"},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Xiao, J., Ye, H., He, X., Zhang, H., Wu, F., and Chua, T.-S. (2017). Attentional factorization machines: Learning the weight of feature interactions via attention networks. arXiv.","DOI":"10.24963\/ijcai.2017\/435"},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Wang, R., Fu, B., Fu, G., and Wang, M. (2017). Deep & cross network for ad click predictions. Proceedings of the ADKDD\u201917, Association for Computing Machinery.","DOI":"10.1145\/3124749.3124754"},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Yu, Y., Wang, Z., and Yuan, B. (2019). An input-aware factorization machine for sparse prediction. IJCAI, 1466\u20131472.","DOI":"10.24963\/ijcai.2019\/203"},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Wang, X., He, X., Wang, M., Feng, F., and Chua, T.-S. (2019, January 21\u201325). Neural graph collaborative filtering. Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval, Paris, France.","DOI":"10.1145\/3331184.3331267"},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Cheng, W., Shen, Y., and Huang, L. (2020, January 7\u201312). Adaptive factorization network: Learning adaptive-order feature interactions. Proceedings of the AAAI Conference on Artificial Intelligence, New York, NY, USA.","DOI":"10.1609\/aaai.v34i04.5768"},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"He, X., Deng, K., Wang, X., Li, Y., Zhang, Y.D., and Wang, M. (2020, January 25\u201330). Lightgcn: Simplifying and powering graph convolution network for recommendation. Proceedings of the 43rd International ACM SIGIR Conference On Research and Development in Information Retrieval, Xi\u2019an, China.","DOI":"10.1145\/3397271.3401063"},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Mao, K., Zhu, J., Xiao, X., Lu, B., Wang, Z., and He, X. (2021, January 1\u20135). UltraGCN: Ultra simplification of graph convolutional networks for recommendation. Proceedings of the 30th ACM International Conference on Information & Knowledge Management, Queensland, Australia.","DOI":"10.1145\/3459637.3482291"},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Lin, X., Wu, J., Zhou, C., Pan, S., Cao, Y., and Wang, B. (2021, January 19\u201323). Task-adaptive neural process for user cold-start recommendation. Proceedings of the Web Conference 2021, New York, NY, USA.","DOI":"10.1145\/3442381.3449908"},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Zhao, M., Wu, L., Liang, Y., Chen, L., Zhang, J., Deng, Q., Wang, K., Shen, X., Lv, T., and Wu, R. (2022, January 11\u201315). Investigating accuracy-novelty performance for graph-based collaborative filtering. Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval, Madrid, Spain.","DOI":"10.1145\/3477495.3532005"},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"10370","DOI":"10.1007\/s11227-023-05831-x","article-title":"Explainable recommendation based on fusion representation of multi-type feature embedding","volume":"80","author":"Zheng","year":"2024","journal-title":"J. Supercomput."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"228","DOI":"10.1007\/s00530-024-01421-w","article-title":"Text-centered cross-sample fusion network for multimodal sentiment analysis","volume":"30","author":"Huang","year":"2024","journal-title":"Multimed. Syst."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"119640","DOI":"10.1016\/j.ins.2023.119640","article-title":"Face2nodes: Learning facial expression representations with relation-aware dynamic graph convolution networks","volume":"649","author":"Jiang","year":"2023","journal-title":"Inf. Sci."},{"key":"ref_45","first-page":"20","article-title":"A memetic walrus algorithm with expert-guided strategy for adaptive curriculum sequencing","volume":"16","author":"Huang","year":"2026","journal-title":"Hum. Centric Comput. Inf. Sci."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"231","DOI":"10.1016\/j.ins.2018.12.003","article-title":"Learning peer recommendation using attention-driven CNN with interaction tripartite graph","volume":"479","author":"Hu","year":"2019","journal-title":"Inf. Sci."},{"key":"ref_47","first-page":"37","article-title":"A music recommendation model based on users\u2019 long and short term preferences and music emotional attention","volume":"40","author":"Wu","year":"2023","journal-title":"J. Guangdong Univ. Technol."},{"key":"ref_48","first-page":"91","article-title":"Factor-level feature and attribute preference joint learning based session recommendation","volume":"41","author":"Lin","year":"2024","journal-title":"J. Guangdong Univ. Technol."}],"container-title":["Information"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2078-2489\/17\/3\/298\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,3,18]],"date-time":"2026-03-18T09:21:55Z","timestamp":1773825715000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2078-2489\/17\/3\/298"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,3,18]]},"references-count":48,"journal-issue":{"issue":"3","published-online":{"date-parts":[[2026,3]]}},"alternative-id":["info17030298"],"URL":"https:\/\/doi.org\/10.3390\/info17030298","relation":{},"ISSN":["2078-2489"],"issn-type":[{"value":"2078-2489","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,3,18]]}}}