{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,20]],"date-time":"2026-01-20T03:28:49Z","timestamp":1768879729174,"version":"3.49.0"},"publisher-location":"Singapore","reference-count":23,"publisher":"Springer Nature Singapore","isbn-type":[{"value":"9789819755745","type":"print"},{"value":"9789819755752","type":"electronic"}],"license":[{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"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":[],"published-print":{"date-parts":[[2024]]},"DOI":"10.1007\/978-981-97-5575-2_23","type":"book-chapter","created":{"date-parts":[[2024,9,1]],"date-time":"2024-09-01T10:01:54Z","timestamp":1725184914000},"page":"313-326","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Distributed Meta-learning for\u00a0Large-Scale Multi-institution Credit Default Risk Prediction"],"prefix":"10.1007","author":[{"given":"Yue","family":"Zhang","sequence":"first","affiliation":[]},{"given":"Xinxing","family":"Yang","sequence":"additional","affiliation":[]},{"given":"Feng","family":"Zhu","sequence":"additional","affiliation":[]},{"given":"Longfei","family":"Li","sequence":"additional","affiliation":[]},{"given":"Linbo","family":"Jiang","sequence":"additional","affiliation":[]},{"given":"Jun","family":"Zhou","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,9,2]]},"reference":[{"key":"23_CR1","doi-asserted-by":"crossref","unstructured":"Chen, T., Guestrin, C.: XGBoost: a scalable tree boosting system. In: SIGKDD, pp. 785\u2013794 (2016)","DOI":"10.1145\/2939672.2939785"},{"key":"23_CR2","doi-asserted-by":"crossref","unstructured":"Cheng, D., Zhang, Y., et al.: A dynamic default prediction framework for networked-guarantee loans. In: CIKM, pp. 2547\u20132555 (2019)","DOI":"10.1145\/3357384.3357804"},{"key":"23_CR3","doi-asserted-by":"crossref","unstructured":"Dong, M., Yuan, F., Yao, L., Xu, X., Zhu, L.: MAMO: memory-augmented meta-optimization for cold-start recommendation. In: KDD, pp. 688\u2013697 (2020)","DOI":"10.1145\/3394486.3403113"},{"key":"23_CR4","unstructured":"Finn, C., Abbeel, P., Levine, S.: Model-agnostic meta-learning for fast adaptation of deep networks. In: ICML, vol. 70, pp. 1126\u20131135 (2017)"},{"key":"23_CR5","unstructured":"Ke, G., Meng, Q., et al.: LightGBM: a highly efficient gradient boosting decision tree. In: NIPS, pp. 3146\u20133154 (2017)"},{"key":"23_CR6","doi-asserted-by":"crossref","unstructured":"Lee, H., Im, J., Jang, S., Cho, H., Chung, S.: MeLU: Meta-learned user preference estimator for cold-start recommendation. In: SIGKDD, pp. 1073\u20131082 (2019)","DOI":"10.1145\/3292500.3330859"},{"key":"23_CR7","doi-asserted-by":"crossref","unstructured":"Li, J., Zhang, Y., et al.: TAML: time-aware meta learning for cold-start problem in news recommendation. In: SIGIR, pp. 2415\u20132419 (2023)","DOI":"10.1145\/3539618.3592068"},{"key":"23_CR8","doi-asserted-by":"crossref","unstructured":"Li, M., Andersen, D.G., et al.: Scaling distributed machine learning with the parameter server. In: OSDI, pp. 583\u2013598 (2014)","DOI":"10.1145\/2640087.2644155"},{"key":"23_CR9","doi-asserted-by":"crossref","unstructured":"Li, P., Li, R., et al.: Improving multi-scenario learning to rank in e-commerce by exploiting task relationships in the label space. In: CIKM, pp. 2605\u20132612 (2020)","DOI":"10.1145\/3340531.3412713"},{"key":"23_CR10","doi-asserted-by":"crossref","unstructured":"Ma, J., Zhao, Z., et al.: Modeling task relationships in multi-task learning with multi-gate mixture-of-experts. In: SIGKDD, pp. 1930\u20131939 (2018)","DOI":"10.1145\/3219819.3220007"},{"key":"23_CR11","doi-asserted-by":"crossref","unstructured":"Ma, X., Zhao, L., et al.: Entire space multi-task model: an effective approach for estimating post-click conversion rate. In: SIGIR, pp. 1137\u20131140 (2018)","DOI":"10.1145\/3209978.3210104"},{"issue":"2","key":"23_CR12","doi-asserted-by":"publisher","first-page":"149","DOI":"10.1007\/BF02296272","volume":"47","author":"GN Masters","year":"1982","unstructured":"Masters, G.N.: A rasch model for partial credit scoring. Psychometrika 47(2), 149\u2013174 (1982)","journal-title":"Psychometrika"},{"key":"23_CR13","unstructured":"Mishra, N., Rohaninejad, M., Chen, X., Abbeel, P.: A simple neural attentive meta-learner. In: ICLR (2018)"},{"key":"23_CR14","doi-asserted-by":"crossref","unstructured":"Niu, X., Li, B., et al.: Heterogeneous graph augmented multi-scenario sharing recommendation with tree-guided expert networks. In: WSDM, pp. 1038\u20131046 (2021)","DOI":"10.1145\/3437963.3441729"},{"key":"23_CR15","doi-asserted-by":"crossref","unstructured":"Qiao, S., Liu, C., Shen, W., Yuille, A.L.: Few-shot image recognition by predicting parameters from activations. In: CVPR, pp. 7229\u20137238 (2018)","DOI":"10.1109\/CVPR.2018.00755"},{"key":"23_CR16","doi-asserted-by":"crossref","unstructured":"Sheng, X., Zhao, L., et al.: One model to serve all: star topology adaptive recommender for multi-domain CTR prediction. In: CIKM, pp. 4104\u20134113 (2021)","DOI":"10.1145\/3459637.3481941"},{"key":"23_CR17","unstructured":"Snell, J., Swersky, K., Zemel, R.S.: Prototypical networks for few-shot learning. In: NeurIPS 2017, pp. 4077\u20134087 (2017)"},{"key":"23_CR18","doi-asserted-by":"crossref","unstructured":"Tang, H., Liu, J., et\u00a0al.: Progressive layered extraction (PLE): a novel multi-task learning (MTL) model for personalized recommendations. In: RecSys, pp. 269\u2013278 (2020)","DOI":"10.1145\/3383313.3412236"},{"key":"23_CR19","unstructured":"Vinyals, O., Blundell, C., Lillicrap, T., Kavukcuoglu, K., Wierstra, D.: Matching networks for one shot learning. In: NeurIPS 2016, pp. 3630\u20133638 (2016)"},{"key":"23_CR20","doi-asserted-by":"crossref","unstructured":"Wang, R., Fu, B., Fu, G., Wang, M.: Deep & cross network for ad click predictions. In: ADKDD, pp. 1\u20137 (2017)","DOI":"10.1145\/3124749.3124754"},{"key":"23_CR21","doi-asserted-by":"crossref","unstructured":"Zhang, Y., Yang, X., et al.: A task-aware attention-based method for improved meta-learning. In: APWeb-WAIM, vol. 13422, pp. 474\u2013482 (2022)","DOI":"10.1007\/978-3-031-25198-6_35"},{"issue":"10","key":"23_CR22","first-page":"2801","volume":"51","author":"J Zhou","year":"2023","unstructured":"Zhou, J., Cao, Y., Hu, B., Zhang, Z., Chen, C.: Real-time dynamic graph unified learning framework for financial transaction risk management. Acta Electron. Sinica 51(10), 2801\u20132811 (2023)","journal-title":"Acta Electron. Sinica"},{"key":"23_CR23","unstructured":"Zhou, J., bin Hu, B., qiang Zhang, Z., chao Chen, C.: MoGE: graph context enhanced multi-task recommendation method. Acta Electron. Sinica 51(11), 3377\u20133387 (2023)"}],"container-title":["Lecture Notes in Computer Science","Database Systems for Advanced Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-97-5575-2_23","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,9,1]],"date-time":"2024-09-01T10:05:30Z","timestamp":1725185130000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-97-5575-2_23"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024]]},"ISBN":["9789819755745","9789819755752"],"references-count":23,"URL":"https:\/\/doi.org\/10.1007\/978-981-97-5575-2_23","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024]]},"assertion":[{"value":"2 September 2024","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"DASFAA","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Database Systems for Advanced Applications","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Gifu","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Japan","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2024","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2 July 2024","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"5 July 2024","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"15","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"dasfaa2024a","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/www.dasfaa2024.org\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}