{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,12]],"date-time":"2025-12-12T08:46:01Z","timestamp":1765529161352,"version":"3.48.0"},"reference-count":42,"publisher":"Springer Science and Business Media LLC","issue":"4","license":[{"start":{"date-parts":[[2025,11,13]],"date-time":"2025-11-13T00:00:00Z","timestamp":1762992000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2025,11,13]],"date-time":"2025-11-13T00:00:00Z","timestamp":1762992000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"DOI":"10.13039\/501100010023","name":"Natural Science Research of Jiangsu Higher Education Institutions of China","doi-asserted-by":"publisher","award":["22KJA520006"],"award-info":[{"award-number":["22KJA520006"]}],"id":[{"id":"10.13039\/501100010023","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Data Sci. Eng."],"published-print":{"date-parts":[[2025,12]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>Recommender systems utilize data analysis and predictive algorithms to suggest relevant items to users, enhancing their experiences and engagements across various digital platforms, particularly in e-commerce. To obtain satisfactory representations of items and user preferences, many existing studies (multi-modal recommendation approaches) integrate diverse data (e.g., text and images) into the recommendation process to enhance item embeddings. However, the capability of these methods is restricted due to the following problems: (1) insufficient utilization of multi-modal information; (2) lack of deeper and more adequate insights from user-item interactions after multi-modal fusion, as well as the inability to uncover more intricate or hidden knowledge in the users\u2019 modality preference. To address these problems, we propose HUMP, which Highlights Users\u2019 Modality Preference for multi-modal recommender systems, featuring two key components: (1) a users\u2019 modality preference guided data fusion module for integrating users\u2019 modality preference into user and item representations which is more appropriate for recommendation scenarios; (2) a global representation enhancement module, designed to learn the deeper relationships of fused information and enhance the representations through a user-item layered heterogeneous graph. Experiments on real-world datasets demonstrate the superiority of our model over state-of-the-art baselines.<\/jats:p>","DOI":"10.1007\/s41019-025-00309-7","type":"journal-article","created":{"date-parts":[[2025,11,13]],"date-time":"2025-11-13T03:18:41Z","timestamp":1763003921000},"page":"786-799","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["HUMP: Highlighted Users\u2019 Modality Preference for Multi-modal Recommender Systems"],"prefix":"10.1007","volume":"10","author":[{"given":"Shijie","family":"Zhu","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Qian","family":"Zhou","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8256-8331","authenticated-orcid":false,"given":"Wei","family":"Chen","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shangfei","family":"Zheng","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Lei","family":"Zhao","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2025,11,13]]},"reference":[{"key":"309_CR1","doi-asserted-by":"publisher","unstructured":"Zhou H, Zhou X, Zeng Z, Zhang L, Shen Z (2023) A comprehensive survey on multimodal recommender systems: taxonomy, evaluation, and future directions. https:\/\/doi.org\/10.48550\/ARXIV.2302.04473","DOI":"10.48550\/ARXIV.2302.04473"},{"key":"309_CR2","doi-asserted-by":"publisher","unstructured":"Liu Q, Hu J, Xiao Y, Gao J, Zhao X (2023) Multimodal recommender systems: a survey. https:\/\/doi.org\/10.48550\/ARXIV.2302.03883","DOI":"10.48550\/ARXIV.2302.03883"},{"key":"309_CR3","doi-asserted-by":"publisher","unstructured":"Zheng S, Wang W, Qu J, Yin H, Chen W, Zhao L (2023) MMKGR: multi-hop multi-modal knowledge graph reasoning. In: ICDE https:\/\/doi.org\/10.1109\/ICDE55515.2023.00015","DOI":"10.1109\/ICDE55515.2023.00015"},{"key":"309_CR4","doi-asserted-by":"publisher","unstructured":"He R, McAuley JJ (2016) VBPR: visual Bayesian personalized ranking from implicit feedback. In: AAAI. https:\/\/doi.org\/10.1609\/AAAI.V30I1.9973","DOI":"10.1609\/AAAI.V30I1.9973"},{"key":"309_CR5","doi-asserted-by":"publisher","unstructured":"Park C, Kim DH, Oh J, Yu H (2017) Do \u201calso-viewed\u201d products help user rating prediction? In: WWW. https:\/\/doi.org\/10.1145\/3038912.3052581","DOI":"10.1145\/3038912.3052581"},{"key":"309_CR6","doi-asserted-by":"publisher","unstructured":"Xu Q, Shen F, Liu L, Shen HT (2018) Graphcar: content-aware multimedia recommendation with graph autoencoder. In: SIGIR. https:\/\/doi.org\/10.1145\/3209978.3210117","DOI":"10.1145\/3209978.3210117"},{"key":"309_CR7","doi-asserted-by":"publisher","unstructured":"Wei Y, Wang X, Nie L, He X, Hong R, Chua T (2019) MMGCN: multi-modal graph convolution network for personalized recommendation of micro-video. In: ACM MM. https:\/\/doi.org\/10.1145\/3343031.3351034","DOI":"10.1145\/3343031.3351034"},{"issue":"5","key":"309_CR8","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 (2020) MGAT: multimodal graph attention network for recommendation. Inf Process Manag 57(5):102277. https:\/\/doi.org\/10.1016\/J.IPM.2020.102277","journal-title":"Inf Process Manag"},{"key":"309_CR9","doi-asserted-by":"publisher","first-page":"1074","DOI":"10.1109\/TMM.2021.3138298","volume":"25","author":"Q Wang","year":"2023","unstructured":"Wang Q, Wei Y, Yin J, Wu J, Song X, Nie L (2023) Dualgnn: dual graph neural network for multimedia recommendation. IEEE Trans Multim 25:1074\u20131084. https:\/\/doi.org\/10.1109\/TMM.2021.3138298","journal-title":"IEEE Trans Multim"},{"issue":"3s","key":"309_CR10","doi-asserted-by":"publisher","first-page":"138","DOI":"10.1145\/3573010","volume":"19","author":"F Lei","year":"2023","unstructured":"Lei F, Cao Z, Yang Y, Ding Y, Zhang C (2023) Learning the user\u2019s deeper preferences for multi-modal recommendation systems. TOMM 19(3s):138\u2013113818. https:\/\/doi.org\/10.1145\/3573010","journal-title":"TOMM"},{"key":"309_CR11","doi-asserted-by":"publisher","unstructured":"Sun R, Cao X, Zhao Y, Wan J, Zhou K, Zhang F, Wang Z, Zheng K (2020) Multi-modal knowledge graphs for recommender systems. In: CIKM. https:\/\/doi.org\/10.1145\/3340531.3411947","DOI":"10.1145\/3340531.3411947"},{"key":"309_CR12","doi-asserted-by":"publisher","DOI":"10.1109\/TMM.2022.3187556","author":"Z Tao","year":"2022","unstructured":"Tao Z, Liu X, Xia Y, Wang X, Yang L, Huang X, Chua T-S (2022) Self-supervised learning for multimedia recommendation. IEEE Trans Multimedia. https:\/\/doi.org\/10.1109\/TMM.2022.3187556","journal-title":"IEEE Trans Multimedia"},{"key":"309_CR13","doi-asserted-by":"publisher","unstructured":"Wei W, Huang C, Xia L, Zhang C (2023) Multi-modal self-supervised learning for recommendation. In: WWW. https:\/\/doi.org\/10.1145\/3543507.3583206","DOI":"10.1145\/3543507.3583206"},{"key":"309_CR14","doi-asserted-by":"publisher","unstructured":"Zhou X, Zhou H, Liu Y, Zeng Z, Miao C, Wang P, You Y, Jiang F (2023) Bootstrap latent representations for multi-modal recommendation. In: WWW. https:\/\/doi.org\/10.1145\/3543507.3583251","DOI":"10.1145\/3543507.3583251"},{"key":"309_CR15","doi-asserted-by":"publisher","unstructured":"Yi Z, Wang X, Ounis I, MacDonald C (2022) Multi-modal graph contrastive learning for micro-video recommendation. In: SIGIR. https:\/\/doi.org\/10.1145\/3477495.3532027","DOI":"10.1145\/3477495.3532027"},{"key":"309_CR16","doi-asserted-by":"publisher","unstructured":"Yu J, Yin H, Li J, Wang Q, Hung NQV, Zhang X (2021) Self-supervised multi-channel hypergraph convolutional network for social recommendation. In: WWW. https:\/\/doi.org\/10.1145\/3442381.3449844","DOI":"10.1145\/3442381.3449844"},{"key":"309_CR17","doi-asserted-by":"publisher","unstructured":"Wei Y, Wang X, Nie L, He X, Chua T (2021) GRCN: graph-refined convolutional network for multimedia recommendation with implicit feedback. https:\/\/doi.org\/10.1145\/2111.02036","DOI":"10.1145\/2111.02036"},{"key":"309_CR18","doi-asserted-by":"publisher","unstructured":"Chen X, Chen H, Xu H, Zhang Y, Cao Y, Qin Z, Zha H (2019) Personalized fashion recommendation with visual explanations based on multimodal attention network: towards visually explainable recommendation. In: SIGIR. https:\/\/doi.org\/10.1145\/3331184.3331254","DOI":"10.1145\/3331184.3331254"},{"key":"309_CR19","doi-asserted-by":"publisher","unstructured":"Mu Z, Zhuang Y, Tan J, Xiao J, Tang S (2022) Learning hybrid behavior patterns for multimedia recommendation. In: ACM MM. https:\/\/doi.org\/10.1145\/3503161.3548119","DOI":"10.1145\/3503161.3548119"},{"key":"309_CR20","doi-asserted-by":"publisher","unstructured":"Fan Z, Xu K, Dong Z, Peng H, Zhang J, Yu PS (2023) Graph collaborative signals denoising and augmentation for recommendation. In: SIGIR. https:\/\/doi.org\/10.1145\/3539618.3591994","DOI":"10.1145\/3539618.3591994"},{"issue":"4","key":"309_CR21","doi-asserted-by":"publisher","first-page":"72","DOI":"10.1145\/3447239","volume":"54","author":"W Cheng","year":"2022","unstructured":"Cheng W, Song S, Chen C, Hidayati SC, Liu J (2022) Fashion meets computer vision: a survey. ACM Comput Surv 54(4):72\u201317241. https:\/\/doi.org\/10.1145\/3447239","journal-title":"ACM Comput Surv"},{"key":"309_CR22","doi-asserted-by":"publisher","unstructured":"Hidayati SC, Hsu C, Chang Y, Hua K, Fu J, Cheng W (2018) What dress fits me best?: fashion recommendation on the clothing style for personal body shape. In: ACM MM. https:\/\/doi.org\/10.1145\/3240508.3240546","DOI":"10.1145\/3240508.3240546"},{"key":"309_CR23","doi-asserted-by":"publisher","first-page":"2701","DOI":"10.1109\/TMM.2021.3088307","volume":"24","author":"Y Wei","year":"2022","unstructured":"Wei Y, Wang X, He X, Nie L, Rui Y, Chua T (2022) Hierarchical user intent graph network for multimedia recommendation. IEEE Trans Multim 24:2701\u20132712. https:\/\/doi.org\/10.1109\/TMM.2021.3088307","journal-title":"IEEE Trans Multim"},{"key":"309_CR24","doi-asserted-by":"publisher","unstructured":"Xie H, Lo L, Shuai H, Cheng W (2020) Au-assisted graph attention convolutional network for micro-expression recognition. In: ACM MM. https:\/\/doi.org\/10.1145\/3394171.3414012","DOI":"10.1145\/3394171.3414012"},{"issue":"5","key":"309_CR25","doi-asserted-by":"publisher","first-page":"97","DOI":"10.1145\/3535101","volume":"55","author":"S Wu","year":"2023","unstructured":"Wu S, Sun F, Zhang W, Xie X, Cui B (2023) Graph neural networks in recommender systems: a survey. ACM Comput Surv 55(5):97\u201319737. https:\/\/doi.org\/10.1145\/3535101","journal-title":"ACM Comput Surv"},{"key":"309_CR26","doi-asserted-by":"publisher","unstructured":"Liu F, Cheng Z, Sun C, Wang Y, Nie L, Kankanhalli MS (2019) User diverse preference modeling by multimodal attentive metric learning. In: ACM MM. https:\/\/doi.org\/10.1145\/3343031.3350953","DOI":"10.1145\/3343031.3350953"},{"key":"309_CR27","doi-asserted-by":"publisher","unstructured":"Liu S, Chen Z, Liu H, Hu X (2019) User-video co-attention network for personalized micro-video recommendation. In: WWW. https:\/\/doi.org\/10.1145\/3308558.3313513","DOI":"10.1145\/3308558.3313513"},{"key":"309_CR28","doi-asserted-by":"publisher","unstructured":"Wu C, Wu F, Qi T, Zhang C, Huang Y, Xu T (2022) Mm-rec: visiolinguistic model empowered multimodal news recommendation. In: SIGIR. https:\/\/doi.org\/10.1145\/3477495.3531896","DOI":"10.1145\/3477495.3531896"},{"key":"309_CR29","doi-asserted-by":"publisher","unstructured":"Tran N, Lauw HW (2022) Aligning dual disentangled user representations from ratings and textual content. In: SIGKDD. https:\/\/doi.org\/10.1145\/3534678.3539474","DOI":"10.1145\/3534678.3539474"},{"key":"309_CR30","doi-asserted-by":"publisher","first-page":"1067","DOI":"10.1109\/TMM.2021.3111487","volume":"24","author":"J Yi","year":"2022","unstructured":"Yi J, Chen Z (2022) Multi-modal variational graph auto-encoder for recommendation systems. IEEE Trans Multim 24:1067\u20131079. https:\/\/doi.org\/10.1109\/TMM.2021.3111487","journal-title":"IEEE Trans Multim"},{"key":"309_CR31","doi-asserted-by":"publisher","unstructured":"Zhang F, Yuan NJ, Lian D, Xie X, Ma W (2016) Collaborative knowledge base embedding for recommender systems. In: SIGKDD. https:\/\/doi.org\/10.1145\/2939672.2939673","DOI":"10.1145\/2939672.2939673"},{"key":"309_CR32","doi-asserted-by":"publisher","unstructured":"Zhang J, Zhu Y, Liu Q, Wu S, Wang S, Wang L (2021) Mining latent structures for multimedia recommendation. In: ACM MM. https:\/\/doi.org\/10.1145\/3474085.3475259","DOI":"10.1145\/3474085.3475259"},{"key":"309_CR33","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1109\/TIP.2019.2923608","volume":"29","author":"Y Wei","year":"2020","unstructured":"Wei Y, Wang X, Guan W, Nie L, Lin Z, Chen B (2020) Neural multimodal cooperative learning toward micro-video understanding. TIP 29:1\u201314. https:\/\/doi.org\/10.1109\/TIP.2019.2923608","journal-title":"TIP"},{"key":"309_CR34","doi-asserted-by":"publisher","unstructured":"He X, Deng K, Wang X, Li Y, Zhang Y, Wang M (2020) Lightgcn: Simplifying and powering graph convolution network for recommendation. In: SIGIR. https:\/\/doi.org\/10.1145\/3397271.3401063","DOI":"10.1145\/3397271.3401063"},{"key":"309_CR35","doi-asserted-by":"publisher","unstructured":"Wang X, Ji H, Shi C, Wang B, Ye Y, Cui P, Yu PS (2019) Heterogeneous graph attention network. In: WWW. https:\/\/doi.org\/10.1145\/3308558.3313562","DOI":"10.1145\/3308558.3313562"},{"key":"309_CR36","doi-asserted-by":"publisher","unstructured":"Rendle S, Freudenthaler C, Gantner Z, Schmidt-Thieme L (2012) BPR: bayesian personalized ranking from implicit feedback. https:\/\/doi.org\/10.1145\/1205.2618","DOI":"10.1145\/1205.2618"},{"key":"309_CR37","doi-asserted-by":"publisher","unstructured":"He R, McAuley JJ (2016) Ups and downs: modeling the visual evolution of fashion trends with one-class collaborative filtering. In: WWW. https:\/\/doi.org\/10.1145\/2872427.2883037","DOI":"10.1145\/2872427.2883037"},{"key":"309_CR38","doi-asserted-by":"publisher","unstructured":"Ni J, Li J (2019) Justifying recommendations using distantly-labeled reviews and fine-grained aspects. In: EMNLP-IJCNLP. https:\/\/doi.org\/10.18653\/V1\/D19-1018","DOI":"10.18653\/V1\/D19-1018"},{"key":"309_CR39","doi-asserted-by":"publisher","unstructured":"Reimers N, Gurevych I (2019) Sentence-bert: sentence embeddings using siamese bert-networks. In: EMNLP-IJCNLP. https:\/\/doi.org\/10.18653\/V1\/D19-1410","DOI":"10.18653\/V1\/D19-1410"},{"key":"309_CR40","first-page":"249","volume":"9","author":"X Glorot","year":"2010","unstructured":"Glorot X, Bengio Y (2010) Understanding the difficulty of training deep feedforward neural networks. AISTATS JMLR Proc 9:249\u2013256","journal-title":"AISTATS JMLR Proc"},{"key":"309_CR41","unstructured":"Kingma DP, Ba J (2015) Adam: a method for stochastic optimization. In: ICLR, pp 1\u201315"},{"key":"309_CR42","doi-asserted-by":"publisher","unstructured":"Zhou X (2023) Mmrec: simplifying multimodal recommendation. https:\/\/doi.org\/10.48550\/ARXIV.2302.03497","DOI":"10.48550\/ARXIV.2302.03497"}],"container-title":["Data Science and Engineering"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s41019-025-00309-7.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s41019-025-00309-7\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s41019-025-00309-7.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,12,12]],"date-time":"2025-12-12T08:42:10Z","timestamp":1765528930000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s41019-025-00309-7"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,11,13]]},"references-count":42,"journal-issue":{"issue":"4","published-print":{"date-parts":[[2025,12]]}},"alternative-id":["309"],"URL":"https:\/\/doi.org\/10.1007\/s41019-025-00309-7","relation":{},"ISSN":["2364-1185","2364-1541"],"issn-type":[{"type":"print","value":"2364-1185"},{"type":"electronic","value":"2364-1541"}],"subject":[],"published":{"date-parts":[[2025,11,13]]},"assertion":[{"value":"3 January 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"18 July 2025","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"19 July 2025","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"13 November 2025","order":4,"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 no conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}},{"value":"Not applicable.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for publication"}},{"value":"Not applicable.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical approval and consent to participate"}},{"value":"Not applicable.","order":5,"name":"Ethics","group":{"name":"EthicsHeading","label":"Human and animal ethics"}}]}}