{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,23]],"date-time":"2026-02-23T19:48:42Z","timestamp":1771876122518,"version":"3.50.1"},"reference-count":66,"publisher":"Springer Science and Business Media LLC","issue":"3","license":[{"start":{"date-parts":[[2026,2,23]],"date-time":"2026-02-23T00:00:00Z","timestamp":1771804800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2026,2,23]],"date-time":"2026-02-23T00:00:00Z","timestamp":1771804800000},"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":["Cluster Comput"],"published-print":{"date-parts":[[2026,6]]},"DOI":"10.1007\/s10586-026-05949-6","type":"journal-article","created":{"date-parts":[[2026,2,23]],"date-time":"2026-02-23T18:42:59Z","timestamp":1771872179000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["A mixed deep neural network for addressing the cold-start problem in recommender systems"],"prefix":"10.1007","volume":"29","author":[{"given":"Gopal","family":"Behera","sequence":"first","affiliation":[]},{"given":"Sanjaya Kumar","family":"Panda","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2026,2,23]]},"reference":[{"key":"5949_CR1","doi-asserted-by":"crossref","unstructured":"El Alaoui, D., Riffi, J., Sabri, A., Aghoutane, B., Yahyaouy, A., Tairi, H.: Social recommendation system based on heterogeneous graph attention networks. Inter. J. Data Sci. Anal., pages 1\u201317 (2024)","DOI":"10.1007\/s41060-024-00698-4"},{"issue":"3","key":"5949_CR2","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s12046-022-01924-0","volume":"47","author":"G Behera","year":"2022","unstructured":"Behera, G., Nain, N.: Gso-crs: grid search optimization for collaborative recommendation system. S\u0101dhan\u0101 47(3), 1\u201312 (2022)","journal-title":"S\u0101dhan\u0101"},{"key":"5949_CR3","doi-asserted-by":"crossref","unstructured":"He, X., Zhang, H., Kan, M.-Y., Chua, T.-S.: Fast matrix factorization for online recommendation with implicit feedback. In: Proceedings of the 39th International ACM SIGIR conference on Research and Development in Information Retrieval, pages 549\u2013558, (2016)","DOI":"10.1145\/2911451.2911489"},{"key":"5949_CR4","doi-asserted-by":"crossref","unstructured":"Koren, Y.: Factorization meets the neighborhood: a multifaceted collaborative filtering model. In: Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining, pages 426\u2013434, (2008)","DOI":"10.1145\/1401890.1401944"},{"key":"5949_CR5","doi-asserted-by":"crossref","unstructured":"Bennett, J., Lanning, S. et\u00a0al.: The netflix prize. In: Proceedings of KDD cup and workshop, volume 2007, page\u00a035. New York, NY, USA., (2007)","DOI":"10.1145\/1345448.1345459"},{"key":"5949_CR6","doi-asserted-by":"crossref","unstructured":"Rendle, S.: Factorization machines. In: 2010 IEEE International conference on data mining, pages 995\u20131000. IEEE, (2010)","DOI":"10.1109\/ICDM.2010.127"},{"key":"5949_CR7","doi-asserted-by":"crossref","unstructured":"He, X., Liao, L., Zhang, H., Nie, L., Hu, X., Chua, T.-S.: Neural collaborative filtering. In: Proceedings of the 26th international conference on world wide web, pages 173\u2013182, (2017)","DOI":"10.1145\/3038912.3052569"},{"key":"5949_CR8","doi-asserted-by":"crossref","unstructured":"Bai, T., Wen, J.-R., Zhang, J., Zhao, W.X.: A neural collaborative filtering model with interaction-based neighborhood. In: Proceedings of the 2017 ACM on Conference on Information and Knowledge Management, pages 1979\u20131982, (2017)","DOI":"10.1145\/3132847.3133083"},{"key":"5949_CR9","doi-asserted-by":"crossref","unstructured":"Sedhain, S., Menon, A.K., Sanner, S., Xie, L.: Autorec: Autoencoders meet collaborative filtering. In: Proceedings of the 24th international conference on World Wide Web, pages 111\u2013112, (2015)","DOI":"10.1145\/2740908.2742726"},{"key":"5949_CR10","unstructured":"Tang, D., Qin, B., Liu, T., Yang, Y.: User modeling with neural network for review rating prediction. In: Twenty-fourth international joint conference on artificial intelligence, (2015)"},{"issue":"5","key":"5949_CR11","doi-asserted-by":"publisher","first-page":"359","DOI":"10.1016\/0893-6080(89)90020-8","volume":"2","author":"K Hornik","year":"1989","unstructured":"Hornik, K., Stinchcombe, M., White, H.: Multilayer feedforward networks are universal approximators. Neural Netw 2(5), 359\u2013366 (1989)","journal-title":"Neural Netw"},{"issue":"7553","key":"5949_CR12","doi-asserted-by":"publisher","first-page":"436","DOI":"10.1038\/nature14539","volume":"521","author":"Y LeCun","year":"2015","unstructured":"LeCun, Y., Bengio, Y., Hinton, G.: Deep learning. nature 521(7553), 436\u2013444 (2015)","journal-title":"nature"},{"issue":"2","key":"5949_CR13","doi-asserted-by":"publisher","first-page":"443","DOI":"10.1007\/s11257-018-9217-6","volume":"29","author":"I Fern\u00e1ndez-Tob\u00edas","year":"2019","unstructured":"Fern\u00e1ndez-Tob\u00edas, I., Cantador, I., Tomeo, P., Anelli, V.W., Di Noia, T.: Addressing the user cold start with cross-domain collaborative filtering: exploiting item metadata in matrix factorization. User Model. User-Adap. Inter 29(2), 443\u2013486 (2019)","journal-title":"User Model. User-Adap. Inter"},{"key":"5949_CR14","unstructured":"Kula, M.: Metadata embeddings for user and item cold-start recommendations. arXiv preprint arXiv:1507.08439, (2015)"},{"key":"5949_CR15","doi-asserted-by":"crossref","unstructured":"Vasile, F., Smirnova, E., Conneau, A.: Meta-prod2vec: Product embeddings using side-information for recommendation. In: Proceedings of the 10th ACM Conference on Recommender Systems, pages 225\u2013232, (2016)","DOI":"10.1145\/2959100.2959160"},{"key":"5949_CR16","doi-asserted-by":"publisher","first-page":"5474","DOI":"10.1609\/aaai.v33i01.33015474","volume":"33","author":"T Xiao","year":"2019","unstructured":"Xiao, T., Liang, S., Shen, W., Meng, Z.: Bayesian deep collaborative matrix factorization. In Proceedings of the AAAI Conference on Artificial Intelligence 33, 5474\u20135481 (2019)","journal-title":"In Proceedings of the AAAI Conference on Artificial Intelligence"},{"issue":"8","key":"5949_CR17","doi-asserted-by":"publisher","first-page":"30","DOI":"10.1109\/MC.2009.263","volume":"42","author":"Y Koren","year":"2009","unstructured":"Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. Computer 42(8), 30\u201337 (2009)","journal-title":"Computer"},{"key":"5949_CR18","first-page":"27","volume":"1","author":"B Sarwar","year":"2002","unstructured":"Sarwar, B., Karypis, G., Konstan, J., Riedl, J.: Incremental singular value decomposition algorithms for highly scalable recommender systems. Fifth international conference on computer and information science 1, 27\u20138 (2002)","journal-title":"Fifth international conference on computer and information science"},{"key":"5949_CR19","unstructured":"Mnih, A., Salakhutdinov, R.R.: Probabilistic matrix factorization. In: Advances in neural information processing systems, pages 1257\u20131264, (2008)"},{"issue":"4","key":"5949_CR20","doi-asserted-by":"publisher","first-page":"561","DOI":"10.3390\/sym11040561","volume":"11","author":"KS Kim","year":"2019","unstructured":"Kim, K.S., Chang, D.S., Choi, Y.S.: Boosting memory-based collaborative filtering using content-metadata. Symmetry 11(4), 561 (2019)","journal-title":"Symmetry"},{"issue":"17","key":"5949_CR21","doi-asserted-by":"publisher","first-page":"7015","DOI":"10.1007\/s11042-014-1950-1","volume":"74","author":"M Soares","year":"2015","unstructured":"Soares, M., Viana, P.: Tuning metadata for better movie content-based recommendation systems. Multimedia Tools and Applications 74(17), 7015\u20137036 (2015)","journal-title":"Multimedia Tools and Applications"},{"key":"5949_CR22","doi-asserted-by":"crossref","unstructured":"Yoon, Y.C., Lee, J.W.: Movie recommendation using metadata based word2vec algorithm. In: 2018 International Conference on Platform Technology and Service (PlatCon), pages 1\u20136. IEEE, (2018)","DOI":"10.1109\/PlatCon.2018.8472729"},{"key":"5949_CR23","unstructured":"Henk, V., Vahdati, S., Nayyeri, M., Ali, M., Yazdi, H.S., Lehmann, J.: Metaresearch recommendations using knowledge graph embeddings. In: RecNLP workshop of AAAI Conference, (2019)"},{"key":"5949_CR24","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.is.2019.01.008","volume":"83","author":"RM D\u2019Addio","year":"2019","unstructured":"D\u2019Addio, R.M., Marinho, R.S., Manzato, M.G.: Combining different metadata views for better recommendation accuracy. Inf. Syst. 83, 1\u201312 (2019)","journal-title":"Inf. Syst."},{"key":"5949_CR25","unstructured":"Luo, X., Yuan, Y., Zhou, M., Liu, Z., Shang, M.: Non-negative latent factor model based on $$\\beta$$-divergence for recommender systems. IEEE Transactions on Systems, Man, and Cybernetics: Systems, (2019)"},{"issue":"5","key":"5949_CR26","doi-asserted-by":"publisher","first-page":"1844","DOI":"10.1109\/TCYB.2019.2894283","volume":"50","author":"X Luo","year":"2019","unstructured":"Luo, X., Zhou, M.C., Li, S., Lun, H., Shang, M.: Non-negativity constrained missing data estimation for high-dimensional and sparse matrices from industrial applications. IEEE transactions on cybernetics 50(5), 1844\u20131855 (2019)","journal-title":"IEEE transactions on cybernetics"},{"issue":"1","key":"5949_CR27","doi-asserted-by":"publisher","first-page":"131","DOI":"10.1109\/JAS.2018.7511189","volume":"6","author":"M Shang","year":"2018","unstructured":"Shang, M., Luo, X., Liu, Z., Chen, J., Yuan, Y., Zhou, M.C.: Randomized latent factor model for high-dimensional and sparse matrices from industrial applications. IEEE\/CAA Journal of Automatica Sinica 6(1), 131\u2013141 (2018)","journal-title":"IEEE\/CAA Journal of Automatica Sinica"},{"key":"5949_CR28","unstructured":"Wu, D., Luo, X., Shang, M., He, Y., Wang, G., Zhou, M.: A deep latent factor model for high-dimensional and sparse matrices in recommender systems. IEEE Transactions on Systems, Man, and Cybernetics: Systems, (2019)"},{"key":"5949_CR29","doi-asserted-by":"crossref","unstructured":"El\u00a0Alaoui, D., Riffi, J., Aghoutane, B., Sabri, A., Yahyaouy, A., Tairi, H.: Collaborative filtering: comparative study between matrix factorization and neural network method. In: International Conference on Networked Systems, pages 361\u2013367. Springer, (2020)","DOI":"10.1007\/978-3-030-67087-0_24"},{"key":"5949_CR30","doi-asserted-by":"crossref","unstructured":"El\u00a0Alaoui, D., Riffi, J., Aghoutane, B., Sabri, A., Yahyaouy, A., Tairi, H.: Overview of the main recommendation approaches for the scientific articles. In: International Conference on Business Intelligence, pages 107\u2013118. Springer, (2021)","DOI":"10.1007\/978-3-030-76508-8_9"},{"issue":"12","key":"5949_CR31","doi-asserted-by":"publisher","first-page":"190","DOI":"10.3390\/bdcc8120190","volume":"8","author":"D El Alaoui","year":"2024","unstructured":"El Alaoui, D., Riffi, J., Sabri, A., Aghoutane, B., Yahyaouy, A., Tairi, H.: Comparative study of filtering methods for scientific research article recommendations. Big Data and Cognitive Computing 8(12), 190 (2024)","journal-title":"Big Data and Cognitive Computing"},{"issue":"14","key":"5949_CR32","doi-asserted-by":"publisher","first-page":"11679","DOI":"10.1007\/s00521-022-07059-x","volume":"34","author":"D El Alaoui","year":"2022","unstructured":"El Alaoui, D., Riffi, J., Sabri, A., Aghoutane, B., Yahyaouy, A., Tairi, H.: Deep graphsage-based recommendation system: jumping knowledge connections with ordinal aggregation network. Neural Computing and Applications 34(14), 11679\u201311690 (2022)","journal-title":"Neural Computing and Applications"},{"key":"5949_CR33","doi-asserted-by":"publisher","DOI":"10.1109\/access.2024.3477956","author":"D El\u00a0Alaoui","year":"2024","unstructured":"El\u00a0Alaoui, D., Riffi, J., Sabri, A., Aghoutane, B., Yahyaouy, A., Tairi, H.: Contextual recommendations: dynamic graph attention networks with edge adaptation. IEEE Access (2024). https:\/\/doi.org\/10.1109\/access.2024.3477956","journal-title":"IEEE Access"},{"key":"5949_CR34","doi-asserted-by":"publisher","DOI":"10.1016\/j.neunet.2025.107176","volume":"185","author":"D El Alaoui","year":"2025","unstructured":"El Alaoui, D., Riffi, J., Sabri, A., Aghoutane, B., Yahyaouy, A., Tairi, H.: A novel session-based recommendation system using capsule graph neural network. Neural Netw. 185, 107176 (2025)","journal-title":"Neural Netw."},{"key":"5949_CR35","unstructured":"Dureddy, H.V., Kaden, Z.: Handling cold-start collaborative filtering with reinforcement learning. arXiv preprint arXiv:1806.06192, (2018)"},{"key":"5949_CR36","doi-asserted-by":"crossref","unstructured":"Do, T.D.T., Cao, L.: Coupled poisson factorization integrated with user\/item metadata for modeling popular and sparse ratings in scalable recommendation. In: Thirty-second AAAI conference on artificial intelligence, (2018)","DOI":"10.1609\/aaai.v32i1.11689"},{"key":"5949_CR37","unstructured":"Ekstrand, M.D., Tian, M., Azpiazu, I.M., Ekstrand, J.D., Anuyah, O., McNeill, D., Pera, M.S.: All the cool kids, how do they fit in?: Popularity and demographic biases in recommender evaluation and effectiveness. In: Conference on fairness, accountability and transparency, pages 172\u2013186. PMLR, (2018)"},{"key":"5949_CR38","unstructured":"Liu, A., Callvik, J.: Using demographic information to reduce the new user problem in recommender systems. KTH Royal Institute of Technology, (2017)"},{"issue":"7","key":"5949_CR39","first-page":"1969","volume":"10","author":"M Sridevi","year":"2017","unstructured":"Sridevi, M., Rao, R.R.: Decors: A simple and efficient demographic collaborative recommender system for movie recommendation. Advances in Computational Sciences and Technology 10(7), 1969\u20131979 (2017)","journal-title":"Advances in Computational Sciences and Technology"},{"key":"5949_CR40","doi-asserted-by":"crossref","unstructured":"Mittal, P., Jain, A., Majumdar, A.: Metadata based recommender systems. In: 2014 International Conference on Advances in Computing, Communications and Informatics (ICACCI), pages 2659\u20132664. IEEE, (2014)","DOI":"10.1109\/ICACCI.2014.6968531"},{"key":"5949_CR41","doi-asserted-by":"crossref","unstructured":"Shang, J., Sun, M., Collins-Thompson, K.: Demographic inference via knowledge transfer in cross-domain recommender systems. In: 2018 IEEE International Conference on Data Mining (ICDM), pages 1218\u20131223. IEEE, (2018)","DOI":"10.1109\/ICDM.2018.00162"},{"key":"5949_CR42","doi-asserted-by":"crossref","unstructured":"Cheng, Z., Ding, Y., He, X., Zhu, L., Song, X., Kankanhalli, M.S.: A 3ncf: An adaptive aspect attention model for rating prediction. In: IJCAI, pages 3748\u20133754, (2018)","DOI":"10.24963\/ijcai.2018\/521"},{"key":"5949_CR43","doi-asserted-by":"crossref","unstructured":"Wang, C., Blei, D.M.: Collaborative topic modeling for recommending scientific articles. In: Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining, pages 448\u2013456, (2011)","DOI":"10.1145\/2020408.2020480"},{"issue":"2","key":"5949_CR44","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3291060","volume":"37","author":"Z Cheng","year":"2019","unstructured":"Cheng, Z., Chang, X., Zhu, L., Kanjirathinkal, R.C., Kankanhalli, M.: Mmalfm: Explainable recommendation by leveraging reviews and images. ACM Transactions on Information Systems (TOIS) 37(2), 1\u201328 (2019)","journal-title":"ACM Transactions on Information Systems (TOIS)"},{"issue":"3","key":"5949_CR45","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3309546","volume":"37","author":"X Guan","year":"2019","unstructured":"Guan, X., Cheng, Z., He, X., Zhang, Y., Zhu, Z., Peng, Q., Chua, T.-S.: Attentive aspect modeling for review-aware recommendation. ACM Transactions on Information Systems 37(3), 1\u201327 (2019)","journal-title":"ACM Transactions on Information Systems"},{"key":"5949_CR46","doi-asserted-by":"publisher","DOI":"10.1016\/j.jksuci.2021.12.021","author":"G Behera","year":"2022","unstructured":"Behera, G., Nain, N.: Handling data sparsity via item metadata embedding into deep collaborative recommender system. Journal of King Saud University (2022). https:\/\/doi.org\/10.1016\/j.jksuci.2021.12.021","journal-title":"Journal of King Saud University"},{"issue":"7","key":"5949_CR47","first-page":"3637","volume":"14","author":"G Behera","year":"2022","unstructured":"Behera, G., Nain, N.: Deepnnmf: deep nonlinear non-negative matrix factorization to address sparsity problem of collaborative recommender system. Int. J. Inf. Technol. 14(7), 3637\u20133645 (2022)","journal-title":"Int. J. Inf. Technol."},{"key":"5949_CR48","doi-asserted-by":"publisher","DOI":"10.1007\/s11042-023-17029-7","author":"G Behara","year":"2023","unstructured":"Behara, G., Yannam, V.R., Nayyar, A., Bagal, D.K.: Integrating metadata into deep autoencoder for handling prediction task of collaborative recommender system. Multimedia Tools and Applications (2023). https:\/\/doi.org\/10.1007\/s11042-023-17029-7","journal-title":"Multimedia Tools and Applications"},{"key":"5949_CR49","doi-asserted-by":"crossref","unstructured":"Wang, H., Wang, N., Yeung, D.-Y.: Collaborative deep learning for recommender systems. In: Proceedings of the 21th ACM SIGKDD international conference on knowledge discovery and data mining, pages 1235\u20131244, (2015)","DOI":"10.1145\/2783258.2783273"},{"key":"5949_CR50","doi-asserted-by":"crossref","unstructured":"Kim, D., Park, C., Oh, J., Lee, S., Yu, H.: Convolutional matrix factorization for document context-aware recommendation. In: Proceedings of the 10th ACM conference on recommender systems, pages 233\u2013240, (2016)","DOI":"10.1145\/2959100.2959165"},{"key":"5949_CR51","doi-asserted-by":"crossref","unstructured":"Dong, X., Yu, L., Wu, Z., Sun, Y., Yuan, L., Zhang, F.: A hybrid collaborative filtering model with deep structure for recommender systems. In: Proceedings of the AAAI Conference on artificial intelligence, volume\u00a031, (2017)","DOI":"10.1609\/aaai.v31i1.10747"},{"issue":"6","key":"5949_CR52","doi-asserted-by":"publisher","first-page":"1022","DOI":"10.1109\/TKDE.2018.2789443","volume":"30","author":"S Wang","year":"2018","unstructured":"Wang, S., Tang, J., Wang, Y., Liu, H.: Exploring hierarchical structures for recommender systems. IEEE Trans. Knowl. Data Eng. 30(6), 1022\u20131035 (2018)","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"5949_CR53","doi-asserted-by":"crossref","unstructured":"Liu, T., Wang, Z., Tang, J., Yang, S., Huang, G.Y., Liu, Z.: Recommender systems with heterogeneous side information. In: The world wide web conference, pages 3027\u20133033, (2019)","DOI":"10.1145\/3308558.3313580"},{"key":"5949_CR54","doi-asserted-by":"publisher","first-page":"829","DOI":"10.1016\/j.procs.2020.04.090","volume":"171","author":"MF Aljunid","year":"2020","unstructured":"Aljunid, M.F., Dh, M.: An efficient deep learning approach for collaborative filtering recommender system. Procedia Computer Science 171, 829\u2013836 (2020)","journal-title":"Procedia Computer Science"},{"key":"5949_CR55","unstructured":"Chen, Y.: Convolutional neural network for sentence classification. Master\u2019s thesis, University of Waterloo, (2015)"},{"issue":"7","key":"5949_CR56","doi-asserted-by":"publisher","first-page":"1864","DOI":"10.1109\/TKDE.2016.2535367","volume":"28","author":"M Wang","year":"2016","unstructured":"Wang, M., Weijie, F., Hao, S., Tao, D., Xindong, W.: Scalable semi-supervised learning by efficient anchor graph regularization. IEEE Trans. Knowl. Data Eng. 28(7), 1864\u20131877 (2016)","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"5949_CR57","unstructured":"Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980, (2014)"},{"issue":"174","key":"5949_CR58","first-page":"1","volume":"21","author":"R Nakada","year":"2020","unstructured":"Nakada, R., Imaizumi, M.: Adaptive approximation and generalization of deep neural network with intrinsic dimensionality. J. Mach. Learn. Res. 21(174), 1\u201338 (2020)","journal-title":"J. Mach. Learn. Res."},{"issue":"12","key":"5949_CR59","doi-asserted-by":"publisher","first-page":"18553","DOI":"10.1007\/s11042-021-10529-4","volume":"80","author":"R Nahta","year":"2021","unstructured":"Nahta, R., Meena, Y.K., Gopalani, D., Chauhan, G.S.: Embedding metadata using deep collaborative filtering to address the cold start problem for the rating prediction task. Multimedia Tools and Applications 80(12), 18553\u201318581 (2021)","journal-title":"Multimedia Tools and Applications"},{"key":"5949_CR60","unstructured":"Gunawardana, A., Shani, G.: A survey of accuracy evaluation metrics of recommendation tasks. J. Mach. Learn. Res. 10(12), (2009)"},{"key":"5949_CR61","doi-asserted-by":"crossref","unstructured":"Ma, H., Yang, H., Lyu, M.R., King, I.: Sorec: social recommendation using probabilistic matrix factorization. In: Proceedings of the 17th ACM conference on Information and knowledge management, pages 931\u2013940, (2008)","DOI":"10.1145\/1458082.1458205"},{"key":"5949_CR62","doi-asserted-by":"crossref","unstructured":"Singh, A.P., Gordon, G.J.: Relational learning via collective matrix factorization. In: Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining, pages 650\u2013658, (2008)","DOI":"10.1145\/1401890.1401969"},{"key":"5949_CR63","first-page":"1329","volume":"17","author":"N Srebro","year":"2004","unstructured":"Srebro, N., Rennie, J.D.M., Jaakkola, T.S.: Maximum-margin matrix factorization. NIPS 17, 1329\u20131336 (2004)","journal-title":"NIPS"},{"key":"5949_CR64","doi-asserted-by":"crossref","unstructured":"Yan, Y., Moreau, C., Wang, Z., Fan, W., Fu, C.: Transforming movie recommendations with advanced machine learning: A study of nmf, svd, and k-means clustering. In: 2024 4th International Symposium on Computer Technology and Information Science (ISCTIS), pages 178\u2013181. IEEE, (2024)","DOI":"10.1109\/ISCTIS63324.2024.10698876"},{"issue":"10","key":"5949_CR65","doi-asserted-by":"publisher","first-page":"7441","DOI":"10.1007\/s13369-024-09361-3","volume":"50","author":"A Jain","year":"2025","unstructured":"Jain, A., Jain, G., Nagar, S., Singh, P.K., Dhar, J.: Rating distribution-aware deep cognitive convolution matrix factorization for recommendation systems. Arab. J. Sci. Eng. 50(10), 7441\u20137462 (2025)","journal-title":"Arab. J. Sci. Eng."},{"issue":"1","key":"5949_CR66","doi-asserted-by":"publisher","first-page":"461","DOI":"10.32604\/cmes.2025.063973","volume":"144","author":"R Nahta","year":"2025","unstructured":"Nahta, R., Naik, N., Parvatha, S., et al.: A deep collaborative neural generative embedding for rating prediction in movie recommendation systems. Computer Modeling in Engineering & Sciences 144(1), 461 (2025)","journal-title":"Computer Modeling in Engineering & Sciences"}],"container-title":["Cluster Computing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10586-026-05949-6.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10586-026-05949-6","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10586-026-05949-6.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,2,23]],"date-time":"2026-02-23T18:43:08Z","timestamp":1771872188000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10586-026-05949-6"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,2,23]]},"references-count":66,"journal-issue":{"issue":"3","published-print":{"date-parts":[[2026,6]]}},"alternative-id":["5949"],"URL":"https:\/\/doi.org\/10.1007\/s10586-026-05949-6","relation":{},"ISSN":["1386-7857","1573-7543"],"issn-type":[{"value":"1386-7857","type":"print"},{"value":"1573-7543","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,2,23]]},"assertion":[{"value":"3 September 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"26 November 2025","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"13 January 2026","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"23 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":"All authors in this work declared that they have no competing interests.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing Interests"}},{"value":"All authors have mutually consented to participate in this study.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent to participate"}},{"value":"All the authors have consented to the journal publishing this paper.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent to publish"}}],"article-number":"189"}}