{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,22]],"date-time":"2026-04-22T19:38:05Z","timestamp":1776886685381,"version":"3.51.2"},"reference-count":39,"publisher":"Springer Science and Business Media LLC","issue":"4","license":[{"start":{"date-parts":[[2021,9,27]],"date-time":"2021-09-27T00:00:00Z","timestamp":1632700800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2021,9,27]],"date-time":"2021-09-27T00:00:00Z","timestamp":1632700800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"funder":[{"DOI":"10.13039\/501100003725","name":"national research foundation of korea","doi-asserted-by":"publisher","award":["2019R1A2C2004990"],"award-info":[{"award-number":["2019R1A2C2004990"]}],"id":[{"id":"10.13039\/501100003725","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Mach Learn"],"published-print":{"date-parts":[[2022,4]]},"DOI":"10.1007\/s10994-021-06059-7","type":"journal-article","created":{"date-parts":[[2021,9,27]],"date-time":"2021-09-27T14:02:44Z","timestamp":1632751364000},"page":"1409-1430","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":12,"title":["Time-aware tensor decomposition for sparse tensors"],"prefix":"10.1007","volume":"111","author":[{"given":"Dawon","family":"Ahn","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jun-Gi","family":"Jang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8774-6950","authenticated-orcid":false,"given":"U","family":"Kang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2021,9,27]]},"reference":[{"key":"6059_CR1","unstructured":"Acar, E., Kolda, T. G., & Dunlavy, D. M. (2011). All-at-once optimization for coupled matrix and tensor factorizations. arXiv preprint arXiv:11053422"},{"key":"6059_CR2","doi-asserted-by":"crossref","unstructured":"Afshar, A., Ho, J. C., Dilkina, B., Perros, I., Khalil, E. B., Xiong, L., & Sunderam, V. (2017). Cp-ortho: An orthogonal tensor factorization framework for spatio-temporal data. In Proceedings of the 25th ACM SIGSPATIAL international conference on advances in geographic information systems, pp. 1\u20134.","DOI":"10.1145\/3139958.3140047"},{"issue":"2","key":"6059_CR3","doi-asserted-by":"publisher","first-page":"876","DOI":"10.1137\/17M1112303","volume":"39","author":"C Battaglino","year":"2018","unstructured":"Battaglino, C., Ballard, G., & Kolda, T. G. (2018). A practical randomized CP tensor decomposition. SIAM Journal on Matrix Analysis Applications, 39(2), 876\u2013901.","journal-title":"SIAM Journal on Matrix Analysis Applications"},{"key":"6059_CR4","doi-asserted-by":"crossref","unstructured":"Beutel, A., Talukdar, P. P., Kumar, A., Faloutsos, C., Papalexakis, E. E., & Xing, E. P. (2014). Flexifact: Scalable flexible factorization of coupled tensors on hadoop. In Proceedings of the 2014 SIAM international conference on data mining, Philadelphia, Pennsylvania, USA, April 24\u201326, 2014 (pp. 109\u2013117). SIAM.","DOI":"10.1137\/1.9781611973440.13"},{"key":"6059_CR5","doi-asserted-by":"publisher","first-page":"81","DOI":"10.1016\/j.enbuild.2017.01.083","volume":"140","author":"LM Candanedo","year":"2017","unstructured":"Candanedo, L. M., Feldheim, V., & Deramaix, D. (2017). Data driven prediction models of energy use of appliances in a low-energy house. Energy and Buildings, 140, 81\u201397.","journal-title":"Energy and Buildings"},{"key":"6059_CR6","unstructured":"Choi, J. H., & Vishwanathan, S. (2014). Dfacto: Distributed factorization of tensors. In Z. Ghahramani, M. Welling, C. Cortes, N. D. Lawrence, & K. Q. Weinberger(Eds.), Advances in Neural Information Processing Systems 27: Annual Conference on Neural Information Processing Systems 2014, December 8\u201313, 2014, Montreal, Quebec, Canada."},{"key":"6059_CR7","doi-asserted-by":"crossref","unstructured":"de Araujo, M. R., Ribeiro, P. M. P., & Faloutsos, C. (2017). Tensorcast: Forecasting with context using coupled tensors (best paper award). In 2017 IEEE International Conference on Data Mining (ICDM) (pp. 71\u201380). IEEE.","DOI":"10.1109\/ICDM.2017.16"},{"issue":"2","key":"6059_CR8","doi-asserted-by":"publisher","first-page":"10","DOI":"10.1145\/1921632.1921636","volume":"5","author":"DM Dunlavy","year":"2011","unstructured":"Dunlavy, D. M., Kolda, T. G., & Acar, E. (2011). Temporal link prediction using matrix and tensor factorizations. ACM Transactions on Knowledge Discovery from Data (TKDD), 5(2), 10.","journal-title":"ACM Transactions on Knowledge Discovery from Data (TKDD)"},{"key":"6059_CR9","unstructured":"Harshman, R. A., et al. (1970). Foundations of the parafac procedure: Models and conditions for an \u201c explanatory\u201d multimodal factor analysis."},{"key":"6059_CR10","doi-asserted-by":"crossref","unstructured":"Jeon, I., Papalexakis, E. E., Kang, U., & Faloutsos, C. (2015). Haten2: Billion-scale tensor decompositions. In 2015 IEEE 31st international conference on data engineering (pp. 1047\u20131058). IEEE.","DOI":"10.1109\/ICDE.2015.7113355"},{"key":"6059_CR11","doi-asserted-by":"crossref","unstructured":"Kang, U., Papalexakis, E. E., Harpale, A., & Faloutsos, C. (2012). Gigatensor: Scaling tensor analysis up by 100 times\u2014Algorithms and discoveries. In KDD, pp. 316\u2013324.","DOI":"10.1145\/2339530.2339583"},{"key":"6059_CR12","doi-asserted-by":"crossref","unstructured":"Kasai, H. (2016). Online low-rank tensor subspace tracking from incomplete data by CP decomposition using recursive least squares. In 2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) (pp. 2519\u20132523). IEEE.","DOI":"10.1109\/ICASSP.2016.7472131"},{"issue":"3","key":"6059_CR13","doi-asserted-by":"publisher","first-page":"105","DOI":"10.1002\/1099-128X(200005\/06)14:3<105::AID-CEM582>3.0.CO;2-I","volume":"14","author":"HA Kiers","year":"2000","unstructured":"Kiers, H. A. (2000). Towards a standardized notation and terminology in multiway analysis. Journal of Chemometrics: A Journal of the Chemometrics Society, 14(3), 105\u2013122.","journal-title":"Journal of Chemometrics: A Journal of the Chemometrics Society"},{"key":"6059_CR14","unstructured":"Kingma, D. P., & Ba, J. (2014). Adam: A method for stochastic optimization. arXiv preprint arXiv:14126980"},{"key":"6059_CR15","doi-asserted-by":"crossref","unstructured":"Kolda, T. G., Bader, B. W., & Kenny, J. P. (2005). Higher-order web link analysis using multilinear algebra. In Proceedings of the 5th IEEE International Conference on Data Mining (ICDM 2005), 27\u201330 November 2005, Houston, Texas, USA (pp. 242\u2013249). IEEE Computer Society.","DOI":"10.1109\/ICDM.2005.77"},{"key":"6059_CR16","doi-asserted-by":"crossref","unstructured":"Kolda, T. G., & Sun, J. (2008). Scalable tensor decompositions for multi-aspect data mining. In 2008 eighth IEEE international conference on data mining (pp. 363\u2013372). IEEE.","DOI":"10.1109\/ICDM.2008.89"},{"key":"6059_CR17","unstructured":"Lebedev, V., Ganin, Y., Rakhuba, M., Oseledets, I. V., & Lempitsky, V. S. (2015). Speeding-up convolutional neural networks using fine-tuned CP-decomposition. In Y. Bengio & Y. LeCun (Eds.), 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7\u20139, 2015, Conference Track Proceedings."},{"issue":"24","key":"6059_CR18","doi-asserted-by":"crossref","first-page":"4151","DOI":"10.1093\/bioinformatics\/bty490","volume":"34","author":"J Lee","year":"2018","unstructured":"Lee, J., Oh, S., & Sael, L. (2018). GIFT: Guided and interpretable factorization for tensors with an application to large-scale multi-platform cancer analysis. Bioinformatics, 34(24), 4151\u20134158.","journal-title":"Bioinformatics"},{"issue":"4","key":"6059_CR19","first-page":"10","volume":"10","author":"H Liu","year":"2019","unstructured":"Liu, H., Li, Y., Tsang, M., & Liu, Y. (2019). Costco: A neural tensor completion model for sparse tensors. Training, 10(4), 10\u20133.","journal-title":"Training"},{"key":"6059_CR20","doi-asserted-by":"crossref","unstructured":"Maruhashi, K., Guo, F., & Faloutsos, C. (2011). Multiaspectforensics: Pattern mining on large-scale heterogeneous networks with tensor analysis. In 2011 international conference on advances in social networks analysis and mining (pp. 203\u2013210). IEEE.","DOI":"10.1109\/ASONAM.2011.80"},{"key":"6059_CR21","doi-asserted-by":"crossref","unstructured":"Matsubara, Y., Sakurai, Y., Faloutsos, C., Iwata, T., & Yoshikawa, M. (2012). Fast mining and forecasting of complex time-stamped events. In Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining (pp. 271\u2013279). ACM.","DOI":"10.1145\/2339530.2339577"},{"key":"6059_CR22","doi-asserted-by":"crossref","unstructured":"Oh, S., Park, N., Sael, L., & Kang, U. (2018). Scalable tucker factorization for sparse tensors\u2014Algorithms and discoveries. In 34th IEEE International Conference on Data Engineering, ICDE 2018, Paris, France, April 16\u201319, 2018.","DOI":"10.1109\/ICDE.2018.00104"},{"key":"6059_CR23","doi-asserted-by":"crossref","unstructured":"Papalexakis, E. E., Faloutsos, C., & Sidiropoulos, N. D. (2012). Parcube: Sparse parallelizable tensor decompositions. In ECML-PKDD, Springer, Lecture Notes in Computer Science, Vol. 7523, pp. 521\u2013536.","DOI":"10.1007\/978-3-642-33460-3_39"},{"key":"6059_CR24","doi-asserted-by":"crossref","unstructured":"Park, N., Oh, S., & Kang, U. (2017). Fast and scalable distributed Boolean tensor factorization. In 33rd IEEE International Conference on Data Engineering, ICDE 2017, San Diego, CA, USA, April 19\u201322, 2017, pp. 1071\u20131082.","DOI":"10.1109\/ICDE.2017.152"},{"key":"6059_CR25","doi-asserted-by":"crossref","unstructured":"Perros, I., Papalexakis, E. E., Park, H., Vuduc, R. W., Yan, X., deFilippi, C., Stewart, W. F., & Sun, J. (2018). Sustain: Scalable unsupervised scoring for tensors and its application to phenotyping. In: Y. Guo & F. Farooq (Eds.), Proceedings of the 24th ACM SIGKDD international conference on Knowledge Discovery & Data Mining, KDD 2018, London, UK, August 19\u201323, 2018 (pp. 2080\u20132089). ACM.","DOI":"10.1145\/3219819.3219999"},{"key":"6059_CR26","doi-asserted-by":"crossref","unstructured":"Perros, I., Papalexakis, E. E., Wang, F., Vuduc, R. W., Searles, E., Thompson, M., & Sun, J. (2017). Spartan: Scalable PARAFAC2 for large & sparse data. In Proceedings of the 23rd ACM SIGKDD international conference on knowledge discovery and data mining, Halifax, NS, Canada, August 13\u201317, 2017 (pp. 375\u2013384). ACM.","DOI":"10.1145\/3097983.3098014"},{"key":"6059_CR27","doi-asserted-by":"crossref","unstructured":"Rendle, S., Marinho, L. B., Nanopoulos, A., & Schmidt-Thieme, L. (2009). Learning optimal ranking with tensor factorization for tag recommendation. In SIGKDD, pp. 727\u2013736.","DOI":"10.1145\/1557019.1557100"},{"key":"6059_CR28","doi-asserted-by":"crossref","unstructured":"Rendle, S., & Schmidt-Thieme, L. (2010). Pairwise interaction tensor factorization for personalized tag recommendation. In WSDM, pp. 81\u201390.","DOI":"10.1145\/1718487.1718498"},{"issue":"1","key":"6059_CR29","doi-asserted-by":"publisher","first-page":"100","DOI":"10.1109\/TKDE.2016.2610420","volume":"29","author":"K Shin","year":"2016","unstructured":"Shin, K., Sael, L., & Kang, U. (2016). Fully scalable methods for distributed tensor factorization. IEEE Transactions on Knowledge and Data Engineering, 29(1), 100\u2013113.","journal-title":"IEEE Transactions on Knowledge and Data Engineering"},{"key":"6059_CR30","doi-asserted-by":"crossref","unstructured":"Smith, S., & Karypis, G. (2017). Accelerating the tucker decomposition with compressed sparse tensors. In F. F. Rivera, T. F. Pena & J. C. Cabaleiro (Eds.), Euro-Par 2017: Parallel Processing\u201423rd International Conference on Parallel and Distributed Computing, Santiago de Compostela, Spain, August 28\u2013September 1, 2017, Proceedings, Springer, Lecture Notes in Computer Science, Vol. 10417, pp. 653\u2013668.","DOI":"10.1007\/978-3-319-64203-1_47"},{"key":"6059_CR31","doi-asserted-by":"crossref","unstructured":"Song, Q., Huang, X., Ge, H., Caverlee, J., & Hu, X. (2017). Multi-aspect streaming tensor completion. In Proceedings of the 23rd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 435\u2013443.","DOI":"10.1145\/3097983.3098007"},{"key":"6059_CR32","doi-asserted-by":"crossref","unstructured":"Sun, J., Papadimitriou, S., Lin, C., Cao, N., Liu, S., & Qian, W. (2009). Multivis: Content-based social network exploration through multi-way visual analysis. In Proceedings of the SIAM international conference on data mining, SDM 2009, April 30\u2013May 2, 2009, Sparks, Nevada, USA (pp. 1064\u20131075). SIAM.","DOI":"10.1137\/1.9781611972795.91"},{"key":"6059_CR33","doi-asserted-by":"crossref","unstructured":"Sun, J., Papadimitriou, S., & Philip, S. Y. (2006). Window-based tensor analysis on high-dimensional and multi-aspect streams. In Sixth International Conference on Data Mining (ICDM\u201906) (pp. 1076\u20131080). IEEE.","DOI":"10.1109\/ICDM.2006.169"},{"issue":"3","key":"6059_CR34","doi-asserted-by":"publisher","first-page":"476","DOI":"10.1109\/TPAMI.2015.2465901","volume":"38","author":"Y Sun","year":"2015","unstructured":"Sun, Y., Gao, J., Hong, X., Mishra, B., & Yin, B. (2015). Heterogeneous tensor decomposition for clustering via manifold optimization. IEEE Transactions on Pattern Analysis and Machine Intelligence, 38(3), 476\u2013489.","journal-title":"IEEE Transactions on Pattern Analysis and Machine Intelligence"},{"key":"6059_CR35","doi-asserted-by":"crossref","unstructured":"Symeonidis, P. (2016). Matrix and tensor decomposition in recommender systems. In: Proceedings of the 10th ACM conference on recommender systems, pp. 429\u2013430.","DOI":"10.1145\/2959100.2959195"},{"key":"6059_CR36","unstructured":"Yu, H. F., Rao, N., & Dhillon, I. S. (2016). Temporal regularized matrix factorization for high-dimensional time series prediction. In Advances in neural information processing systems, pp. 847\u2013855."},{"issue":"2205","key":"6059_CR37","doi-asserted-by":"publisher","first-page":"20170457","DOI":"10.1098\/rspa.2017.0457","volume":"473","author":"S Zhang","year":"2017","unstructured":"Zhang, S., Guo, B., Dong, A., He, J., Xu, Z., & Chen, S. X. (2017). Cautionary tales on air-quality improvement in Beijing. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences, 473(2205), 20170457.","journal-title":"Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences"},{"key":"6059_CR38","unstructured":"Zhou, H., Zhang, D., Xie, K., & Chen, Y. (2015). Spatio-temporal tensor completion for imputing missing internet traffic data. In 2015 IEEE 34th international performance computing and communications conference (IPCCC) (pp. 1\u20137). IEEE."},{"key":"6059_CR39","doi-asserted-by":"crossref","unstructured":"Zhou, S., Erfani, S., & Bailey, J. (2018). Online CP decomposition for sparse tensors. In 2018 IEEE International Conference on Data Mining (ICDM) (pp. 1458\u20131463). IEEE.","DOI":"10.1109\/ICDM.2018.00202"}],"container-title":["Machine Learning"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10994-021-06059-7.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10994-021-06059-7\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10994-021-06059-7.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,11,9]],"date-time":"2023-11-09T12:40:17Z","timestamp":1699533617000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10994-021-06059-7"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,9,27]]},"references-count":39,"journal-issue":{"issue":"4","published-print":{"date-parts":[[2022,4]]}},"alternative-id":["6059"],"URL":"https:\/\/doi.org\/10.1007\/s10994-021-06059-7","relation":{},"ISSN":["0885-6125","1573-0565"],"issn-type":[{"value":"0885-6125","type":"print"},{"value":"1573-0565","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,9,27]]},"assertion":[{"value":"30 September 2020","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"8 July 2021","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"6 August 2021","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"27 September 2021","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":"Conflict of interest"}},{"value":"This content has been made available to all.","name":"free","label":"Free to read"}]}}