{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,14]],"date-time":"2026-04-14T00:55:26Z","timestamp":1776128126382,"version":"3.50.1"},"reference-count":39,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T00:00:00Z","timestamp":1780272000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T00:00:00Z","timestamp":1780272000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T00:00:00Z","timestamp":1780272000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-017"},{"start":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T00:00:00Z","timestamp":1780272000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T00:00:00Z","timestamp":1780272000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-012"},{"start":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T00:00:00Z","timestamp":1780272000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T00:00:00Z","timestamp":1780272000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-004"}],"funder":[{"DOI":"10.13039\/501100003725","name":"National Research Foundation of Korea","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100003725","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100014188","name":"Ministry of Science and ICT, South Korea","doi-asserted-by":"publisher","award":["RS-2021-NR059723"],"award-info":[{"award-number":["RS-2021-NR059723"]}],"id":[{"id":"10.13039\/501100014188","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100014188","name":"Ministry of Science and ICT, South Korea","doi-asserted-by":"publisher","award":["RS-2023-00220762"],"award-info":[{"award-number":["RS-2023-00220762"]}],"id":[{"id":"10.13039\/501100014188","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Engineering Applications of Artificial Intelligence"],"published-print":{"date-parts":[[2026,6]]},"DOI":"10.1016\/j.engappai.2026.114485","type":"journal-article","created":{"date-parts":[[2026,3,16]],"date-time":"2026-03-16T13:06:38Z","timestamp":1773666398000},"page":"114485","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":0,"special_numbering":"C","title":["Training-free adjustable polynomial graph filtering for ultra-fast multimodal recommendation"],"prefix":"10.1016","volume":"173","author":[{"given":"Yu-Seung","family":"Roh","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Joo-Young","family":"Kim","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jin-Duk","family":"Park","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6533-3469","authenticated-orcid":false,"given":"Won-Yong","family":"Shin","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"78","reference":[{"key":"10.1016\/j.engappai.2026.114485_b1","series-title":"46th Annual IEEE Symposium on Foundations of Computer Science","first-page":"339","article-title":"Fast algorithms for approximate semidefinite programming using the multiplicative weights update method","author":"Arora","year":"2005"},{"key":"10.1016\/j.engappai.2026.114485_b2","series-title":"Noise Reduction Speech Processing","first-page":"1","article-title":"Pearson correlation coefficient","author":"Cohen","year":"2009"},{"key":"10.1016\/j.engappai.2026.114485_b3","series-title":"Lanczos Algorithms for Large Symmetric Eigenvalue Computations: Vol. I: Theory","author":"Cullum","year":"2002"},{"key":"10.1016\/j.engappai.2026.114485_b4","unstructured":"Defferrard, M., Bresson, X., Vandergheynst, P., 2016. Convolutional Neural Networks on Graphs with Fast Localized Spectral Filtering. In: Proc. Int. Conf. Neural Inf. Process. Syst.. Barcelona, Spain, pp. 3837\u20133845."},{"issue":"2","key":"10.1016\/j.engappai.2026.114485_b5","doi-asserted-by":"crossref","first-page":"221","DOI":"10.1137\/S0895480191222653","article-title":"The Laplacian spectrum of a graph II","volume":"7","author":"Grone","year":"1994","journal-title":"SIAM J. Discret. Math."},{"key":"10.1016\/j.engappai.2026.114485_b6","series-title":"Proc. 38th AAAI Conf. Artif. Intell.","first-page":"8454","article-title":"LGMRec: Local and global graph learning for multimodal recommendation","author":"Guo","year":"2024"},{"key":"10.1016\/j.engappai.2026.114485_b7","doi-asserted-by":"crossref","unstructured":"He, X., Deng, K., Wang, X., Li, Y., Zhang, Y., Wang, M., 2020. LightGCN: Simplifying and Powering Graph Convolution Network for Recommendation. In: Proc. 43rd Int. ACM Conf. Res. Develop. Inf. Retrieval. SIGIR\u201920, Virtual Event, pp. 639\u2013648. http:\/\/dx.doi.org\/10.1145\/3397271.3401063.","DOI":"10.1145\/3397271.3401063"},{"key":"10.1016\/j.engappai.2026.114485_b8","doi-asserted-by":"crossref","unstructured":"He, R., McAuley, J.J., 2016a. Ups and Downs: Modeling the Visual Evolution of Fashion Trends with One-Class Collaborative Filtering. In: Proc. the Web Conf.. WWW\u201916, Montreal, Canada, pp. 507\u2013517. http:\/\/dx.doi.org\/10.1145\/2872427.2883037.","DOI":"10.1145\/2872427.2883037"},{"key":"10.1016\/j.engappai.2026.114485_b9","doi-asserted-by":"crossref","unstructured":"He, R., McAuley, J.J., 2016b. VBPR: Visual Bayesian Personalized Ranking from Implicit Feedback. In: Proc. 30th AAAI Conf. Artif. Intell.. AAAI\u201916, Phoenix, AZ, pp. 144\u2013150. http:\/\/dx.doi.org\/10.1609\/AAAI.V30I1.9973.","DOI":"10.1609\/aaai.v30i1.9973"},{"key":"10.1016\/j.engappai.2026.114485_b10","doi-asserted-by":"crossref","first-page":"4745","DOI":"10.1109\/TSP.2024.3349788","article-title":"Graph filters for signal processing and machine learning on graphs","volume":"72","author":"Isufi","year":"2024","journal-title":"IEEE Trans. Signal Process."},{"issue":"8","key":"10.1016\/j.engappai.2026.114485_b11","doi-asserted-by":"crossref","DOI":"10.1371\/journal.pone.0135090","article-title":"Using dynamic multi-task non-negative matrix factorization to detect the evolution of user preferences in collaborative filtering","volume":"10","author":"Ju","year":"2015","journal-title":"PLoS One"},{"key":"10.1016\/j.engappai.2026.114485_b12","series-title":"Proc. the Web Conf.","first-page":"1077","article-title":"Leveraging member-group relations via multi-view graph filtering for effective group recommendation","author":"Kim","year":"2025"},{"key":"10.1016\/j.engappai.2026.114485_b13","series-title":"Proc. of 17th ACM Int. Conf. Web Search and Data Mining","first-page":"332","article-title":"MONET: Modality-embracing graph convolutional network and target-aware attention for multimedia recommendation","author":"Kim","year":"2024"},{"key":"10.1016\/j.engappai.2026.114485_b14","unstructured":"Kipf, T.N., Welling, M., 2017. Semi-Supervised Classification with Graph Convolutional Networks. In: Proc. 5th Int. Conf. Learn. Representations. ICLR\u201917, Toulon, France."},{"key":"10.1016\/j.engappai.2026.114485_b15","doi-asserted-by":"crossref","unstructured":"Liu, J., Li, D., Gu, H., Lu, T., Zhang, P., Shang, L., Gu, N., 2023. Personalized Graph Signal Processing for Collaborative Filtering. In: Proc. the Web Conf.. WWW\u201923, Austin, TX, pp. 1264\u20131272. http:\/\/dx.doi.org\/10.1145\/3543507.3583466.","DOI":"10.1145\/3543507.3583466"},{"key":"10.1016\/j.engappai.2026.114485_b16","doi-asserted-by":"crossref","unstructured":"McAuley, J.J., Targett, C., Shi, Q., van den Hengel, A., 2015. Image-Based Recommendations on Styles and Substitutes. In: Proc. 38th Int. ACM Conf. Res. Develop. Inf. Retrieval. SIGIR\u201915, Santiago, Chile, pp. 43\u201352. http:\/\/dx.doi.org\/10.1145\/2766462.2767755.","DOI":"10.1145\/2766462.2767755"},{"issue":"5","key":"10.1016\/j.engappai.2026.114485_b17","doi-asserted-by":"crossref","first-page":"808","DOI":"10.1109\/JPROC.2018.2820126","article-title":"Graph signal processing: Overview, challenges, and applications","volume":"106","author":"Ortega","year":"2018","journal-title":"Proc. IEEE"},{"key":"10.1016\/j.engappai.2026.114485_b18","doi-asserted-by":"crossref","unstructured":"Park, J., Shin, Y., Shin, W., 2024. Turbo-CF: Matrix Decomposition-Free Graph Filtering for Fast Recommendation. In: Proc. 47th Int. ACM Conf. Res. Develop. Inf. Retrieval. SIGIR\u201924, Washington D.C., USA, pp. 2672\u20132676. http:\/\/dx.doi.org\/10.1145\/3626772.3657916.","DOI":"10.1145\/3626772.3657916"},{"key":"10.1016\/j.engappai.2026.114485_b19","series-title":"Proc. the Web Conf.","first-page":"4482","article-title":"Criteria-aware graph filtering: Extremely fast yet accurate multi-criteria recommendation","author":"Park","year":"2025"},{"key":"10.1016\/j.engappai.2026.114485_b20","series-title":"Proc. 47th Int. ACM Conf. Res. Develop. Inf. Retrieval","first-page":"2142","article-title":"Why is normalization necessary for linear recommenders?","author":"Park","year":"2025"},{"issue":"4","key":"10.1016\/j.engappai.2026.114485_b21","first-page":"56:1","article-title":"Balancing embedding spectrum for recommendation","volume":"3","author":"Peng","year":"2025","journal-title":"Trans. Recomm. Syst."},{"issue":"11","key":"10.1016\/j.engappai.2026.114485_b22","doi-asserted-by":"crossref","first-page":"1745","DOI":"10.1007\/s10994-018-5740-2","article-title":"On analyzing user preference dynamics with temporal social networks","volume":"107","author":"Pereira","year":"2018","journal-title":"Mach. Learn."},{"key":"10.1016\/j.engappai.2026.114485_b23","doi-asserted-by":"crossref","first-page":"58","DOI":"10.1016\/j.engappai.2015.08.010","article-title":"An imputation-based matrix factorization method for improving accuracy of collaborative filtering systems","volume":"46","author":"Ranjbar","year":"2015","journal-title":"Eng. Appl. Artif. Intell."},{"key":"10.1016\/j.engappai.2026.114485_b24","doi-asserted-by":"crossref","unstructured":"Reimers, N., Gurevych, I., 2019. Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks. In: Proc. Conf. on Empirical Methods Natural Lang. Process., 9th Int. Joint Conf. Natural Lang. Process.. EMNLP-IJCNLP\u201919, Hong Kong, China, pp. 3980\u20133990. http:\/\/dx.doi.org\/10.18653\/V1\/D19-1410.","DOI":"10.18653\/v1\/D19-1410"},{"key":"10.1016\/j.engappai.2026.114485_b25","series-title":"BPR: Bayesian personalized ranking from implicit feedback","author":"Rendle","year":"2012"},{"key":"10.1016\/j.engappai.2026.114485_b26","doi-asserted-by":"crossref","unstructured":"Shen, Y., Wu, Y., Zhang, Y., Shan, C., Zhang, J., Letaief, K.B., Li, D., 2021. How Powerful is Graph Convolution for Recommendation?. In: Proc. 30th Int. Conf. Inf. Knowl. Manage.. CIKM\u201921, Virtual Event, pp. 1619\u20131629. http:\/\/dx.doi.org\/10.1145\/3459637.3482264.","DOI":"10.1145\/3459637.3482264"},{"issue":"3","key":"10.1016\/j.engappai.2026.114485_b27","doi-asserted-by":"crossref","first-page":"83","DOI":"10.1109\/MSP.2012.2235192","article-title":"The emerging field of signal processing on graphs: Extending high-dimensional data analysis to networks and other irregular domains","volume":"30","author":"Shuman","year":"2013","journal-title":"IEEE Signal Process. Mag."},{"key":"10.1016\/j.engappai.2026.114485_b28","doi-asserted-by":"crossref","first-page":"347","DOI":"10.1016\/j.engappai.2019.06.020","article-title":"Collaborative filtering embeddings for memory-based recommender systems","volume":"85","author":"Valcarce","year":"2019","journal-title":"Eng. Appl. Artif. Intell."},{"key":"10.1016\/j.engappai.2026.114485_b29","doi-asserted-by":"crossref","unstructured":"Wang, X., He, X., Wang, M., Feng, F., Chua, T., 2019. Neural Graph Collaborative Filtering. In: Proc. 42nd Int. ACM Conf. Res. Develop. Inf. Retrieval. SIGIR\u201919, Paris, France, pp. 165\u2013174. http:\/\/dx.doi.org\/10.1145\/3331184.3331267.","DOI":"10.1145\/3331184.3331267"},{"key":"10.1016\/j.engappai.2026.114485_b30","doi-asserted-by":"crossref","unstructured":"Wei, Y., Wang, X., Nie, L., He, X., Chua, T., 2020. Graph-Refined Convolutional Network for Multimedia Recommendation with Implicit Feedback. In: Proc. 28th ACM Int. Conf. Multimedia. MM\u201920, Virtual Event, pp. 3541\u20133549. http:\/\/dx.doi.org\/10.1145\/3394171.3413556.","DOI":"10.1145\/3394171.3413556"},{"key":"10.1016\/j.engappai.2026.114485_b31","series-title":"Proc. the Web Conf.","first-page":"2360","article-title":"FIRE: Fast incremental recommendation with graph signal processing","author":"Xia","year":"2022"},{"key":"10.1016\/j.engappai.2026.114485_b32","series-title":"Proc. of 37th Int. Conf. on Machine Learning, ICML 2020, 13-18 July 2020, Virtual Event","first-page":"10936","article-title":"Graph convolutional network for recommendation with low-pass collaborative filters","volume":"vol. 119","author":"Yu","year":"2020"},{"key":"10.1016\/j.engappai.2026.114485_b33","doi-asserted-by":"crossref","unstructured":"Yu, P., Tan, Z., Lu, G., Bao, B., 2023. Multi-View Graph Convolutional Network for Multimedia Recommendation. In: Proc. 31st ACM Int. Conf. Multimedia. MM\u201923, Ottawa, ON, Canada, pp. 6576\u20136585. http:\/\/dx.doi.org\/10.1145\/3581783.3613915.","DOI":"10.1145\/3581783.3613915"},{"key":"10.1016\/j.engappai.2026.114485_b34","series-title":"Proc. 39th AAAI Conf. Artif. Intell.","first-page":"13096","article-title":"Mind individual information! principal graph learning for multimedia recommendation","author":"Yu","year":"2025"},{"key":"10.1016\/j.engappai.2026.114485_b35","doi-asserted-by":"crossref","unstructured":"Zhang, J., Zhu, Y., Liu, Q., Wu, S., Wang, S., Wang, L., 2021. Mining Latent Structures for Multimedia Recommendation. In: Proc. 29th ACM Int. Conf. Multimedia. MM\u201921, Virtual Event, pp. 3872\u20133880. http:\/\/dx.doi.org\/10.1145\/3474085.3475259.","DOI":"10.1145\/3474085.3475259"},{"key":"10.1016\/j.engappai.2026.114485_b36","doi-asserted-by":"crossref","unstructured":"Zhou, X., 2023. MMRec: Simplifying Multimodal Recommendation. In: Proc. 5th Multimedia Asia Workshops. MMAsia\u201923, Tainan, Taiwan, pp. 6:1\u20136:2. http:\/\/dx.doi.org\/10.1145\/3611380.3628561.","DOI":"10.1145\/3611380.3628561"},{"key":"10.1016\/j.engappai.2026.114485_b37","doi-asserted-by":"crossref","unstructured":"Zhou, X., Shen, Z., 2023. A Tale of Two Graphs: Freezing and Denoising Graph Structures for Multimodal Recommendation. In: Proc. 31st ACM Int. Conf. Multimedia. MM\u201923, Ottawa, ON, Canada, pp. 935\u2013943. http:\/\/dx.doi.org\/10.1145\/3581783.3611943.","DOI":"10.1145\/3581783.3611943"},{"key":"10.1016\/j.engappai.2026.114485_b38","doi-asserted-by":"crossref","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: Proc. the Web Conf.. WWW\u201923, Austin, TX, pp. 845\u2013854. http:\/\/dx.doi.org\/10.1145\/3543507.3583251.","DOI":"10.1145\/3543507.3583251"},{"key":"10.1016\/j.engappai.2026.114485_b39","series-title":"A comprehensive survey on multimodal recommender systems: Taxonomy, evaluation, and future directions","author":"Zhou","year":"2023"}],"container-title":["Engineering Applications of Artificial Intelligence"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0952197626007669?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0952197626007669?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,4,14]],"date-time":"2026-04-14T00:09:56Z","timestamp":1776125396000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0952197626007669"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,6]]},"references-count":39,"alternative-id":["S0952197626007669"],"URL":"https:\/\/doi.org\/10.1016\/j.engappai.2026.114485","relation":{},"ISSN":["0952-1976"],"issn-type":[{"value":"0952-1976","type":"print"}],"subject":[],"published":{"date-parts":[[2026,6]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Training-free adjustable polynomial graph filtering for ultra-fast multimodal recommendation","name":"articletitle","label":"Article Title"},{"value":"Engineering Applications of Artificial Intelligence","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.engappai.2026.114485","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2026 Elsevier Ltd. All rights are reserved, including those for text and data mining, AI training, and similar technologies.","name":"copyright","label":"Copyright"}],"article-number":"114485"}}