{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,31]],"date-time":"2026-07-31T19:55:22Z","timestamp":1785527722614,"version":"3.56.0"},"reference-count":68,"publisher":"Springer Science and Business Media LLC","issue":"2","license":[{"start":{"date-parts":[[2024,2,12]],"date-time":"2024-02-12T00:00:00Z","timestamp":1707696000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,2,12]],"date-time":"2024-02-12T00:00:00Z","timestamp":1707696000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/100012543","name":"Shanghai Science and Technology Development Fund","doi-asserted-by":"crossref","award":["No. 19DZ1200802"],"award-info":[{"award-number":["No. 19DZ1200802"]}],"id":[{"id":"10.13039\/100012543","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China Projects","doi-asserted-by":"crossref","award":["No. U1936213"],"award-info":[{"award-number":["No. U1936213"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["World Wide Web"],"published-print":{"date-parts":[[2024,3]]},"DOI":"10.1007\/s11280-024-01251-w","type":"journal-article","created":{"date-parts":[[2024,2,12]],"date-time":"2024-02-12T10:04:52Z","timestamp":1707732292000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["LAMEE: a light all-MLP framework for time series prediction empowering recommendations"],"prefix":"10.1007","volume":"27","author":[{"given":"Yi","family":"Xie","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yun","family":"Xiong","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiaofeng","family":"Gao","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jiadong","family":"Chen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yao","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xian","family":"Wu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chao","family":"Chen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2024,2,12]]},"reference":[{"key":"1251_CR1","doi-asserted-by":"crossref","unstructured":"Jannach, D., Zanker, M., Felfernig, A., Friedrich, G.: Recommender systems: an introduction (2010)","DOI":"10.1017\/CBO9780511763113"},{"issue":"8","key":"1251_CR2","doi-asserted-by":"publisher","first-page":"3549","DOI":"10.1109\/TKDE.2020.3028705","volume":"34","author":"Q Guo","year":"2020","unstructured":"Guo, Q., Zhuang, F., Qin, C., Zhu, H., Xie, X., Xiong, H., He, Q.: A survey on knowledge graph-based recommender systems. IEEE Trans. Knowl. Data Eng. 34(8), 3549\u20133568 (2020)","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"1251_CR3","doi-asserted-by":"crossref","unstructured":"Sun, R., Cao, X., Zhao, Y., Wan, J., Zhou, K., Zhang, F., Wang, Z., Zheng, K.: Multi-modal knowledge graphs for recommender systems. In: Proceedings of the 29th ACM International Conference on Information & Knowledge Management, pp. 1405\u20131414 (2020)","DOI":"10.1145\/3340531.3411947"},{"key":"1251_CR4","doi-asserted-by":"crossref","unstructured":"Huang, C., Xu, H., Xu, Y., Dai, P., Xia, L., Lu, M., Bo, L., Xing, H., Lai, X., Ye, Y.: Knowledge-aware coupled graph neural network for social recommendation. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 4115\u20134122 (2021)","DOI":"10.1609\/aaai.v35i5.16533"},{"issue":"12","key":"1251_CR5","doi-asserted-by":"publisher","first-page":"16761","DOI":"10.1007\/s11042-022-12564-1","volume":"81","author":"M Sharaf","year":"2022","unstructured":"Sharaf, M., Hemdan, E.E.-D., El-Sayed, A., El-Bahnasawy, N.A.: A survey on recommendation systems for financial services. Multimed. Tools Appl. 81(12), 16761\u201316781 (2022)","journal-title":"Multimed. Tools Appl."},{"key":"1251_CR6","doi-asserted-by":"publisher","first-page":"54527","DOI":"10.1109\/ACCESS.2018.2871131","volume":"6","author":"J Xue","year":"2018","unstructured":"Xue, J., Zhu, E., Liu, Q., Yin, J.: Group recommendation based on financial social network for robo-advisor. IEEE Access 6, 54527\u201354535 (2018)","journal-title":"IEEE Access"},{"key":"1251_CR7","doi-asserted-by":"publisher","first-page":"101597","DOI":"10.1016\/j.tele.2021.101597","volume":"61","author":"M Nilashi","year":"2021","unstructured":"Nilashi, M., Asadi, S., Minaei-Bidgoli, B., Abumalloh, R.A., Samad, S., Ghabban, F., Ahani, A.: Recommendation agents and information sharing through social media for coronavirus outbreak. Telemat. Inform. 61, 101597 (2021)","journal-title":"Telemat. Inform."},{"key":"1251_CR8","doi-asserted-by":"crossref","unstructured":"Hussain, M.M.-u., Avci, B., Trajcevski, G., Scheuermann, P.: Incorporating weather updates for public transportation users of recommendation systems. In: 2016 17th IEEE International Conference on Mobile Data Management (MDM), vol. 1, pp. 333\u2013336 (2016). IEEE","DOI":"10.1109\/MDM.2016.57"},{"issue":"1","key":"1251_CR9","doi-asserted-by":"publisher","first-page":"52","DOI":"10.3141\/2423-07","volume":"2423","author":"S Djavadian","year":"2014","unstructured":"Djavadian, S., Hoogendoorn, R.G., Van Arerm, B., Chow, J.Y.: Empirical evaluation of drivers\u2019 route choice behavioral responses to social navigation. Transportation research record 2423(1), 52\u201360 (2014)","journal-title":"Transportation research record"},{"key":"1251_CR10","doi-asserted-by":"crossref","unstructured":"Hussain, M.M.-u., Avci, B., Trajcevski, G., Scheuermann, P.: Incorporating weather updates for public transportation users of recommendation systems. In: 2016 17th IEEE International Conference on Mobile Data Management (MDM), vol. 1, pp. 333\u2013336 (2016). IEEE","DOI":"10.1109\/MDM.2016.57"},{"issue":"10","key":"1251_CR11","doi-asserted-by":"publisher","first-page":"2566","DOI":"10.1109\/TKDE.2016.2580511","volume":"28","author":"H Yin","year":"2016","unstructured":"Yin, H., Zhou, X., Cui, B., Wang, H., Zheng, K., Nguyen, Q.V.H.: Adapting to user interest drift for poi recommendation. IEEE Trans. Knowl. Data Eng. 28(10), 2566\u20132581 (2016)","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"1251_CR12","unstructured":"Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, \u0141, Polosukhin, I.: Attention is all you need. Adv. Neural Inf. Process. Syst. 30 (2017)"},{"key":"1251_CR13","doi-asserted-by":"crossref","unstructured":"Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11106\u201311115 (2021)","DOI":"10.1609\/aaai.v35i12.17325"},{"key":"1251_CR14","first-page":"22419","volume":"34","author":"H Wu","year":"2021","unstructured":"Wu, H., Xu, J., Wang, J., Long, M.: Autoformer: Decomposition transformers with auto-correlation for long-term series forecasting. Adv. Neural Inf. Process. Syst. 34, 22419\u201322430 (2021)","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"1251_CR15","unstructured":"Zhou, T., Ma, Z., Wen, Q., Wang, X., Sun, L., Jin, R.: Fedformer: Frequency enhanced decomposed transformer for long-term series forecasting. International Conference on Learning Representations (2022)"},{"key":"1251_CR16","unstructured":"Liu, S., Yu, H., Liao, C., Li, J., Lin, W., Liu, A.X., Dustdar, S.: Pyraformer: Low-complexity pyramidal attention for long-range time series modeling and forecasting. In: International Conference on Learning Representations (2021)"},{"issue":"12","key":"1251_CR17","first-page":"2346","volume":"31","author":"J Lu","year":"2018","unstructured":"Lu, J., Liu, A., Dong, F., Gu, F., Gama, J., Zhang, G.: Learning under concept drift: A review. IEEE Trans. Knowl. Data Eng. 31(12), 2346\u20132363 (2018)","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"1251_CR18","doi-asserted-by":"publisher","first-page":"106031","DOI":"10.1016\/j.cie.2019.106031","volume":"137","author":"J Zenisek","year":"2019","unstructured":"Zenisek, J., Holzinger, F., Affenzeller, M.: Machine learning based concept drift detection for predictive maintenance. Comput. Ind. Eng. 137, 106031 (2019)","journal-title":"Comput. Ind. Eng."},{"issue":"3","key":"1251_CR19","doi-asserted-by":"publisher","first-page":"2081","DOI":"10.1007\/s11063-022-11015-0","volume":"55","author":"C Ding","year":"2023","unstructured":"Ding, C., Zhao, J., Sun, S.: Concept drift adaptation for time series anomaly detection via transformer. Neural Process. Lett. 55(3), 2081\u20132101 (2023)","journal-title":"Neural Process. Lett."},{"issue":"14","key":"1251_CR20","doi-asserted-by":"publisher","first-page":"6322","DOI":"10.3390\/app11146322","volume":"11","author":"Z Zhao","year":"2021","unstructured":"Zhao, Z., Xu, J., Zang, Y., Hu, R.: Adaptive abnormal oil temperature diagnosis method of transformer based on concept drift. Appl. Sci. 11(14), 6322 (2021)","journal-title":"Appl. Sci."},{"issue":"2","key":"1251_CR21","first-page":"291","volume":"1","author":"WA Trybulec","year":"1990","unstructured":"Trybulec, W.A.: Vectors in real linear space. Formalized Math. 1(2), 291\u2013296 (1990)","journal-title":"Formalized Math."},{"key":"1251_CR22","unstructured":"Brand, L.: Vector and Tensor Analysis. Courier Dover Publications, ??? (2020)"},{"key":"1251_CR23","unstructured":"Arjovsky, M., Bottou, L.: Towards principled methods for training generative adversarial networks. arXiv:1701.04862 (2017)"},{"key":"1251_CR24","unstructured":"Pall\u00e1s-Areny, R., Webster, J.G.: Analog Signal Processing. John Wiley & Sons, ??? (1999)"},{"key":"1251_CR25","unstructured":"Orfanidis, S.J.: Introduction to Signal Processing. Prentice-Hall, Inc., ??? (1995)"},{"key":"1251_CR26","doi-asserted-by":"publisher","first-page":"100255","DOI":"10.1016\/j.cosrev.2020.100255","volume":"37","author":"S Kulkarni","year":"2020","unstructured":"Kulkarni, S., Rodd, S.F.: Context aware recommendation systems: A review of the state of the art techniques. Comput. Sci. Rev. 37, 100255 (2020)","journal-title":"Comput. Sci. Rev."},{"issue":"2","key":"1251_CR27","first-page":"91","volume":"17","author":"GE Box","year":"1968","unstructured":"Box, G.E., Jenkins, G.M.: Some recent advances in forecasting and control. J. R. Stat. Soc. Ser. C Appl. Stat. 17(2), 91\u2013109 (1968)","journal-title":"J. R. Stat. Soc. Ser. C Appl. Stat."},{"key":"1251_CR28","unstructured":"Box, G.E., Jenkins, G.M., Reinsel, G.C., Ljung, G.M.: Time Series Analysis: Forecasting and Control. John Wiley & Sons, ??? (2015)"},{"issue":"1","key":"1251_CR29","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1002\/for.3980040103","volume":"4","author":"ES Gardner Jr","year":"1985","unstructured":"Gardner, E.S., Jr.: Exponential smoothing: The state of the art. J. Forecast. 4(1), 1\u201328 (1985)","journal-title":"J. Forecast."},{"key":"1251_CR30","doi-asserted-by":"crossref","unstructured":"Jalles, J.T.: Structural time series models and the kalman filter: a concise review (2009)","DOI":"10.2139\/ssrn.1496864"},{"key":"1251_CR31","doi-asserted-by":"crossref","unstructured":"Friedman, J.H.: Greedy function approximation: a gradient boosting machine. Ann. Stat. 1189\u20131232 (2001)","DOI":"10.1214\/aos\/1013203451"},{"key":"1251_CR32","unstructured":"Elsayed, S., Thyssens, D., Rashed, A., Jomaa, H.S., Schmidt-Thieme, L.: Do we really need deep learning models for time series forecasting? arXiv:2101.02118 (2021)"},{"key":"1251_CR33","unstructured":"Wen, R., Torkkola, K., Narayanaswamy, B., Madeka, D.: A multi-horizon quantile recurrent forecaster. arXiv:1711.11053 (2017)"},{"key":"1251_CR34","unstructured":"Rangapuram, S.S., Seeger, M.W., Gasthaus, J., Stella, L., Wang, Y., Januschowski, T.: Deep state space models for time series forecasting. Adv. Neural Inf. Process. Syst. 31 (2018)"},{"key":"1251_CR35","unstructured":"Maddix, D.C., Wang, Y., Smola, A.: Deep factors with gaussian processes for forecasting. arXiv:1812.00098 (2018)"},{"key":"1251_CR36","unstructured":"Oord, A.v.d., Dieleman, S., Zen, H., Simonyan, K., Vinyals, O., Graves, A., Kalchbrenner, N., Senior, A., Kavukcuoglu, K.: Wavenet: A generative model for raw audio. arXiv:1609.03499 (2016)"},{"key":"1251_CR37","unstructured":"Borovykh, A., Bohte, S., Oosterlee, C.W.: Conditional time series forecasting with convolutional neural networks. arXiv:1703.04691 (2017)"},{"key":"1251_CR38","unstructured":"Bai, S., Kolter, J.Z., Koltun, V.: An empirical evaluation of generic convolutional and recurrent networks for sequence modeling. arXiv:1803.01271 (2018)"},{"issue":"5","key":"1251_CR39","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":"2","key":"1251_CR40","first-page":"40","volume":"2","author":"DW Ruck","year":"1990","unstructured":"Ruck, D.W., Rogers, S.K., Kabrisky, M.: Feature selection using a multilayer perceptron. J. Neural Netw. Comput. 2(2), 40\u201348 (1990)","journal-title":"J. Neural Netw. Comput."},{"issue":"5","key":"1251_CR41","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":"6","key":"1251_CR42","doi-asserted-by":"publisher","first-page":"84","DOI":"10.1145\/3065386","volume":"60","author":"A Krizhevsky","year":"2012","unstructured":"Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Commun. ACM 60(6), 84\u201390 (2012)","journal-title":"Commun. ACM"},{"key":"1251_CR43","doi-asserted-by":"publisher","first-page":"1735","DOI":"10.1162\/neco.1997.9.8.1735","volume":"9","author":"S Hochreiter","year":"1997","unstructured":"Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Comput. 9, 1735\u20131780 (1997)","journal-title":"Neural Comput."},{"issue":"10s","key":"1251_CR44","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3505244","volume":"54","author":"S Khan","year":"2022","unstructured":"Khan, S., Naseer, M., Hayat, M., Zamir, S.W., Khan, F.S., Shah, M.: Transformers in vision: A survey. ACM Comput. Surv. 54(10s), 1\u201341 (2022)","journal-title":"ACM Comput. Surv."},{"key":"1251_CR45","unstructured":"Tay, Y., Dehghani, M., Bahri, D., Metzler, D.: Efficient transformers: A survey. ACM Computing Surveys (CSUR) (2020)"},{"key":"1251_CR46","unstructured":"Ying, C., Cai, T., Luo, S., Zheng, S., Ke, G., He, D., Shen, Y., Liu, T.-Y.: Do transformers really perform bad for graph representation? In: Neural Information Processing Systems (2021)"},{"key":"1251_CR47","doi-asserted-by":"crossref","unstructured":"Wen, Q., Zhou, T., Zhang, C., Chen, W., Ma, Z., Yan, J., Sun, L.: Transformers in time series: A survey. arXiv:2202.07125 (2022)","DOI":"10.24963\/ijcai.2023\/759"},{"issue":"7","key":"1251_CR48","doi-asserted-by":"publisher","DOI":"10.1016\/j.patter.2022.100520","volume":"3","author":"R Liu","year":"2022","unstructured":"Liu, R., Li, Y., Tao, L., Liang, D., Zheng, H.-T.: Are we ready for a new paradigm shift? a survey on visual deep mlp. Patterns 3(7), 100520 (2022)","journal-title":"Patterns"},{"key":"1251_CR49","first-page":"24261","volume":"34","author":"IO Tolstikhin","year":"2021","unstructured":"Tolstikhin, I.O., Houlsby, N., Kolesnikov, A., Beyer, L., Zhai, X., Unterthiner, T., Yung, J., Steiner, A., Keysers, D., Uszkoreit, J., et al.: Mlp-mixer: An all-mlp architecture for vision. Adv. Neural Inf. Process. Syst. 34, 24261\u201324272 (2021)","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"1251_CR50","unstructured":"Melas-Kyriazi, L.: Do you even need attention? a stack of feed-forward layers does surprisingly well on imagenet. arXiv:2105.02723 (2021)"},{"key":"1251_CR51","unstructured":"Hendrycks, D., Gimpel, K.: Gaussian error linear units (gelus). arXiv:1606.08415 (2016)"},{"key":"1251_CR52","unstructured":"Nussbaum, M.: Advanced digital signal processing and noise reduction (2016)"},{"key":"1251_CR53","unstructured":"Persons, W.M.: Indices of business conditions: an index of general business conditions (1919)"},{"key":"1251_CR54","unstructured":"Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? arXiv:2205.13504 (2022)"},{"key":"1251_CR55","doi-asserted-by":"crossref","unstructured":"Wu, Z., Pan, S., Long, G., Jiang, J., Chang, X., Zhang, C.: Connecting the dots: Multivariate time series forecasting with graph neural networks. In: Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 753\u2013763 (2020)","DOI":"10.1145\/3394486.3403118"},{"key":"1251_CR56","unstructured":"Liu, Y., Wu, H., Wang, J., Long, M.: Non-stationary transformers: Exploring the stationarity in time series forecasting. In: Advances in Neural Information Processing Systems (2022)"},{"key":"1251_CR57","unstructured":"Woo, G., Liu, C., Sahoo, D., Kumar, A., Hoi, S.: Etsformer: Exponential smoothing transformers for time-series forecasting. arXiv:2202.01381 (2022)"},{"key":"1251_CR58","unstructured":"Wang, H., Peng, J., Huang, F., Wang, J., Chen, J., Xiao, Y.: Micn: Multi-scale local and global context modeling for long-term series forecasting. In: The Eleventh International Conference on Learning Representations (2023)"},{"key":"1251_CR59","doi-asserted-by":"crossref","unstructured":"Zhang, T., Zhang, Y., Cao, W., Bian, J., Yi, X., Zheng, S., Li, J.: Less is more: Fast multivariate time series forecasting with light sampling-oriented mlp structures. arXiv:2207.01186 (2022)","DOI":"10.1155\/2022\/5596676"},{"key":"1251_CR60","unstructured":"Zhang, Y., Yan, J.: Crossformer: Transformer utilizing cross-dimension dependency for multivariate time series forecasting. In: The Eleventh International Conference on Learning Representations (2023)"},{"key":"1251_CR61","unstructured":"Wu, H., Hu, T., Liu, Y., Zhou, H., Wang, J., Long, M.: Timesnet: Temporal 2d-variation modeling for general time series analysis (2023)"},{"key":"1251_CR62","unstructured":"Yu, P., Artetxe, M., Ott, M., Shleifer, S., Gong, H., Stoyanov, V., Li, X.: Efficient language modeling with sparse all-mlp. arXiv:2203.06850 (2022)"},{"key":"1251_CR63","unstructured":"Navon, D., Bronstein, A.M.: Transformer vs. mlp-mixer exponential expressive gap for nlp problems. arXiv:2208.08191 (2022)"},{"key":"1251_CR64","doi-asserted-by":"publisher","first-page":"1379","DOI":"10.1007\/s10479-019-03284-1","volume":"299","author":"P D\u2019Urso","year":"2021","unstructured":"D\u2019Urso, P., De Giovanni, L., Massari, R.: Trimmed fuzzy clustering of financial time series based on dynamic time warping. Ann. Oper. Res. 299, 1379\u20131395 (2021)","journal-title":"Ann. Oper. Res."},{"issue":"6","key":"1251_CR65","doi-asserted-by":"publisher","first-page":"S38","DOI":"10.1016\/j.acap.2013.08.002","volume":"13","author":"RB Penfold","year":"2013","unstructured":"Penfold, R.B., Zhang, F.: Use of interrupted time series analysis in evaluating health care quality improvements. Acad. Pediatr. 13(6), S38\u2013S44 (2013)","journal-title":"Acad. Pediatr."},{"key":"1251_CR66","doi-asserted-by":"publisher","DOI":"10.1016\/j.apenergy.2021.117114","volume":"298","author":"S Ding","year":"2021","unstructured":"Ding, S., Li, R., Wu, S., Zhou, W.: Application of a novel structure-adaptative grey model with adjustable time power item for nuclear energy consumption forecasting. Appl. Energy 298, 117114 (2021)","journal-title":"Appl. Energy"},{"key":"1251_CR67","doi-asserted-by":"crossref","unstructured":"Zhao, J., Chen, C., Zhang, W., Li, R., Gu, F., Guo, S., Luo, J., Zheng, Y.: Coupling makes better: an intertwined neural network for taxi and ridesourcing demand co-prediction. IEEE Transactions on Intelligent Transportation Systems. IEEE (2023)","DOI":"10.1109\/TITS.2023.3312224"},{"issue":"4","key":"1251_CR68","doi-asserted-by":"publisher","DOI":"10.1007\/s11704-022-2900-0","volume":"16","author":"Z-H Zhou","year":"2022","unstructured":"Zhou, Z.-H.: Rehearsal: Learning from prediction to decision. Front. Comput. Sci. 16(4), 164352 (2022)","journal-title":"Front. Comput. Sci."}],"container-title":["World Wide Web"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11280-024-01251-w.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11280-024-01251-w\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11280-024-01251-w.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,3,21]],"date-time":"2024-03-21T10:12:36Z","timestamp":1711015956000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11280-024-01251-w"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,2,12]]},"references-count":68,"journal-issue":{"issue":"2","published-print":{"date-parts":[[2024,3]]}},"alternative-id":["1251"],"URL":"https:\/\/doi.org\/10.1007\/s11280-024-01251-w","relation":{"has-preprint":[{"id-type":"doi","id":"10.21203\/rs.3.rs-3750712\/v1","asserted-by":"object"}]},"ISSN":["1386-145X","1573-1413"],"issn-type":[{"value":"1386-145X","type":"print"},{"value":"1573-1413","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,2,12]]},"assertion":[{"value":"14 December 2023","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"19 January 2024","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"27 January 2024","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"12 February 2024","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"This study did not involve any experiments on human or animal subjects. Therefore, ethical approval was not required.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical approval"}},{"value":"The authors declare no competing interests.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"13"}}