{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,2,21]],"date-time":"2025-02-21T22:27:25Z","timestamp":1740176845489,"version":"3.37.3"},"reference-count":47,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2021,5,17]],"date-time":"2021-05-17T00:00:00Z","timestamp":1621209600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2021,5,17]],"date-time":"2021-05-17T00:00:00Z","timestamp":1621209600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"funder":[{"DOI":"10.13039\/100000001","name":"National Science Foundation","doi-asserted-by":"publisher","award":["1920920","1937010"],"award-info":[{"award-number":["1920920","1937010"]}],"id":[{"id":"10.13039\/100000001","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000001","name":"National Science Foundation","doi-asserted-by":"publisher","award":["1940093","1946391"],"award-info":[{"award-number":["1940093","1946391"]}],"id":[{"id":"10.13039\/100000001","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Int J Data Sci Anal"],"published-print":{"date-parts":[[2021,6]]},"DOI":"10.1007\/s41060-021-00263-3","type":"journal-article","created":{"date-parts":[[2021,5,17]],"date-time":"2021-05-17T10:04:23Z","timestamp":1621245863000},"page":"1-14","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Enhancing personalized modeling via weighted and adversarial learning"],"prefix":"10.1007","volume":"12","author":[{"given":"Wei","family":"Du","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2823-3063","authenticated-orcid":false,"given":"Xintao","family":"Wu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2021,5,17]]},"reference":[{"key":"263_CR1","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., and Sun, J.: \u201cDeep residual learning for image recognition,\u201d in IEEE CVPR, (2016)","DOI":"10.1109\/CVPR.2016.90"},{"key":"263_CR2","doi-asserted-by":"crossref","unstructured":"Lai, S., Xu, L., Liu, K., and Zhao, J.: \u201cRecurrent convolutional neural networks for text classification,\u201d in AAAI, (2015)","DOI":"10.1609\/aaai.v29i1.9513"},{"key":"263_CR3","doi-asserted-by":"crossref","unstructured":"Dong, X., Yu, L., Wu, Z., Sun, Y., Yuan, L., and Zhang, F.: \u201cA hybrid collaborative filtering model with deep structure for recommender systems,\u201d in AAAI, (2017)","DOI":"10.1609\/aaai.v31i1.10747"},{"key":"263_CR4","unstructured":"Dean, J., Corrado, G., Monga, R., Chen, K., Devin, M., Mao, M., Senior, A., Tucker, P., Yang, K., Le , Q.V. etal.: \u201cLarge scale distributed deep networks,\u201d in NeurIPS, (2012)"},{"key":"263_CR5","doi-asserted-by":"crossref","unstructured":"Park, D.H., Kim, H.K., Choi, I.Y., and Kim, J.K.: \u201cA literature review and classification of recommender systems research,\u201d Expert systems with applications, (2012)","DOI":"10.1016\/j.eswa.2012.02.038"},{"key":"263_CR6","doi-asserted-by":"crossref","unstructured":"Cheng, Y., Wang, F., Zhang, P., and Hu, J.: \u201cRisk prediction with electronic health records: A deep learning approach,\u201d in SDM, (2016)","DOI":"10.1137\/1.9781611974348.49"},{"key":"263_CR7","unstructured":"Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., and Bengio, Y.: \u201cGenerative adversarial nets,\u201d in NeurIPS, (2014)"},{"key":"263_CR8","unstructured":"Chilimbi, T., Suzue, Y., Apacible, J., and Kalyanaraman, K.: \u201cProject adam: Building an efficient and scalable deep learning training system,\u201d in OSDI, (2014)"},{"key":"263_CR9","unstructured":"Wen, W., Xu, C., Yan, F., Wu, C., Wang, Y., Chen, Y., and Li, H.: \u201cTerngrad: Ternary gradients to reduce communication in distributed deep learning,\u201d in NeurIPS, (2017)"},{"key":"263_CR10","doi-asserted-by":"crossref","unstructured":"Chen, C.-Y., Choi, J., Brand, D., Agrawal, A., Zhang, W., and Gopalakrishnan, K.: \u201cAdacomp: Adaptive residual gradient compression for data-parallel distributed training,\u201d in AAAI, (2018)","DOI":"10.1609\/aaai.v32i1.11728"},{"key":"263_CR11","doi-asserted-by":"crossref","unstructured":"Wang, S., Pi, A., Zhao, X., and Zhou, X.: \u201cScalable distributed dl training: Batching communication and computation,\u201d in AAAI, (2019)","DOI":"10.1609\/aaai.v33i01.33015289"},{"key":"263_CR12","unstructured":"Wangni, J., Wang, J., Liu, J., and Zhang, T.: \u201cGradient sparsification for communication-efficient distributed optimization,\u201d in NeurIPS, (2018)"},{"key":"263_CR13","unstructured":"McMahan, H.B., Moore, E., Ramage, D., Hampson, S. etal.: \u201cCommunication-efficient learning of deep networks from decentralized data,\u201d in AISTATS, (2016)"},{"key":"263_CR14","doi-asserted-by":"crossref","unstructured":"Bonawitz, K., Ivanov, V., Kreuter, B., Marcedone, A., McMahan, H.B., Patel, S., Ramage, D., Segal, A., and Seth, K.: \u201cPractical secure aggregation for privacy-preserving machine learning,\u201d in ACM CCS, (2017)","DOI":"10.1145\/3133956.3133982"},{"key":"263_CR15","unstructured":"Smith, V., Chiang, C.-K., Sanjabi, M., and Talwalkar, A.S.: \u201cFederated multi-task learning,\u201d in NeurIPS, (2017)"},{"key":"263_CR16","doi-asserted-by":"crossref","unstructured":"Che, C., Xiao, C., Liang, J., Jin, B., Zho, J., and Wang, F.: \u201cAn rnn architecture with dynamic temporal matching for personalized predictions of parkinson\u2019s disease,\u201d in SDM, (2017)","DOI":"10.1137\/1.9781611974973.23"},{"key":"263_CR17","doi-asserted-by":"crossref","unstructured":"Suo, Q., Ma, F., Yuan, Y., Huai, M., Zhong, W., Zhang, A., and Gao, J.: \u201cPersonalized disease prediction using a cnn-based similarity learning method,\u201d in IEEE BIBM, (2017)","DOI":"10.1109\/BIBM.2017.8217759"},{"key":"263_CR18","doi-asserted-by":"crossref","unstructured":"Choi, E., Bahadori, M.T., Searles, E., Coffey, C., Thompson, M., Bost, J., Tejedor-Sojo, J., and Sun, J.: \u201cMulti-layer representation learning for medical concepts,\u201d in ACM KDD, (2016)","DOI":"10.1145\/2939672.2939823"},{"key":"263_CR19","doi-asserted-by":"crossref","unstructured":"Huai, M., Miao, C., Suo, Q., Li, Y., Gao, J. and Zhang, A.: \u201cUncorrelated patient similarity learning,\u201d in SDM, (2018)","DOI":"10.1137\/1.9781611975321.31"},{"issue":"1","key":"263_CR20","doi-asserted-by":"publisher","first-page":"54","DOI":"10.1002\/sam.11135","volume":"5","author":"F Wang","year":"2012","unstructured":"Wang, F., Sun, J., Ebadollahi, S.: Composite distance metric integration by leveraging multiple experts\u2019 inputs and its application in patient similarity assessment. Stat Anal Data Mining ASA Data Sci J 5(1), 54\u201369 (2012)","journal-title":"Stat Anal Data Mining ASA Data Sci J"},{"key":"263_CR21","doi-asserted-by":"crossref","unstructured":"Li, M., and Wang, L.: \u201cA survey on personalized news recommendation technology,\u201d IEEE Access, (2019)","DOI":"10.1109\/ACCESS.2019.2944927"},{"issue":"1","key":"263_CR22","doi-asserted-by":"publisher","first-page":"95","DOI":"10.1109\/TSG.2017.2732346","volume":"10","author":"F Luo","year":"2017","unstructured":"Luo, F., Ranzi, G., Wang, X., Dong, Z.Y.: Social information filtering-based electricity retail plan recommender system for smart grid end users. IEEE Trans Smart Grid 10(1), 95\u2013104 (2017)","journal-title":"IEEE Trans Smart Grid"},{"key":"263_CR23","doi-asserted-by":"crossref","unstructured":"Kouki, P., Fakhraei, S., Foulds, J., Eirinaki, M., and Getoor, L.: \u201cHyper: A flexible and extensible probabilistic framework for hybrid recommender systems,\u201d in ACM RecSys, (2015)","DOI":"10.1145\/2792838.2800175"},{"key":"263_CR24","doi-asserted-by":"crossref","unstructured":"Hu, L., Cao, L., Wang, S., Xu, G., Cao, J., and Gu, Z.: \u201cDiversifying personalized recommendation with user-session context.\u201d in IJCAI, (2017), pp. 1858\u20131864","DOI":"10.24963\/ijcai.2017\/258"},{"key":"263_CR25","doi-asserted-by":"crossref","unstructured":"Yu, Z., Lian, J., Mahmoody, A., Liu, G., and Xie, X.: \u201cAdaptive user modeling with long and short-term preferences for personalized recommendation.\u201d in IJCAI, (2019), pp. 4213\u20134219","DOI":"10.24963\/ijcai.2019\/585"},{"key":"263_CR26","doi-asserted-by":"crossref","unstructured":"Bengio, Y., Courville, A., and Vincent, P.: \u201cRepresentation learning: A review and new perspectives,\u201d IEEE TPAMI, (2013)","DOI":"10.1109\/TPAMI.2013.50"},{"key":"263_CR27","doi-asserted-by":"crossref","unstructured":"Tzeng, E., Hoffman, J., Darrell, T., and Saenko, K.: \u201cSimultaneous deep transfer across domains and tasks,\u201d in IEEE CVPR, (2015)","DOI":"10.1109\/ICCV.2015.463"},{"key":"263_CR28","unstructured":"Liu, A.H., Liu, Y.-C., Yeh, Y.-Y., and Wang, Y.-C.F.: \u201cA unified feature disentangler for multi-domain image translation and manipulation,\u201d in NeurIPS, (2018)"},{"key":"263_CR29","unstructured":"Gupta, A., Devin, C., Liu, Y., Abbeel, P., and Levine, S.: \u201cLearning invariant feature spaces to transfer skills with reinforcement learning,\u201d in ICLR, (2017)"},{"key":"263_CR30","doi-asserted-by":"crossref","unstructured":"Misra, I., Shrivastava, A., Gupta, A., and Hebert, M.: \u201cCross-stitch networks for multi-task learning,\u201d in IEEE CVPR, (2016)","DOI":"10.1109\/CVPR.2016.433"},{"key":"263_CR31","doi-asserted-by":"crossref","unstructured":"Bouchacourt, D., Tomioka, R., and Nowozin, S.: \u201cMulti-level variational autoencoder: Learning disentangled representations from grouped observations,\u201d in AAAI, (2018)","DOI":"10.1609\/aaai.v32i1.11867"},{"key":"263_CR32","unstructured":"Narayanaswamy, S., Paige, T.B., Vande Meent, J.-W., Desmaison, A., Goodman, N., Kohli, P., Wood, F., and Torr, P.: \u201cLearning disentangled representations with semi-supervised deep generative models,\u201d in NeurIPS, (2017)"},{"key":"263_CR33","doi-asserted-by":"publisher","unstructured":"Zadrozny, B.: \u201cLearning and evaluating classifiers under sample selection bias,\u201d in Machine Learning, Proceedings of the Twenty-first International Conference (ICML 2004), Banff, Alberta, Canada, July 4-8, 2004, ser. ACM International Conference Proceeding Series, C.E. Brodley, Ed., vol.69.ACM, 2004. [Online]. Available: https:\/\/doi.org\/10.1145\/1015330.1015425","DOI":"10.1145\/1015330.1015425"},{"key":"263_CR34","unstructured":"Wen, J., Yu, C.-N., and Greiner, R.: \u201cRobust learning under uncertain test distributions: Relating covariate shift to model misspecification.\u201d in ICML, (2014), pp. 631\u2013639"},{"key":"263_CR35","doi-asserted-by":"crossref","unstructured":"Khodabandeh, M., Vahdat, A., Ranjbar, M., and Macready, W.G.: \u201cA robust learning approach to domain adaptive object detection,\u201d in Proceedings of the IEEE International Conference on Computer Vision, (2019), pp. 480\u2013490","DOI":"10.1109\/ICCV.2019.00057"},{"key":"263_CR36","unstructured":"Wang, X., and Schneider, J.: \u201cFlexible transfer learning under support and model shift,\u201d in Advances in Neural Information Processing Systems, 2014, pp. 1898\u20131906"},{"key":"263_CR37","unstructured":"Huang, J., Gretton, A., Borgwardt, K., Sch\u00f6lkopf, B., and Smola, A.J.: \u201cCorrecting sample selection bias by unlabeled data,\u201d in Advances in neural information processing systems, (2007), pp. 601\u2013608"},{"key":"263_CR38","unstructured":"Gretton, A., Borgwardt, K., Rasch, M., Sch\u00f6lkopf, B., and Smola, A.J.: \u201cA kernel method for the two-sample-problem,\u201d in Advances in neural information processing systems, (2007), pp. 513\u2013520"},{"key":"263_CR39","doi-asserted-by":"crossref","unstructured":"Schonlau, M., DuMouchel, W., Ju, W.-H., Karr, A.F., Theusan, M., Vardi, Y., etal.:, \u201cComputer intrusion: Detecting masquerades,\u201d Statistical science, (2001)","DOI":"10.1214\/ss\/998929476"},{"key":"263_CR40","doi-asserted-by":"crossref","unstructured":"Phan, N., Ebrahimi, J., Kil, D., Piniewski, B., and Dou, D.: \u201cTopic-aware physical activity propagation in a health social network,\u201d IEEE intelligent systems, (2015)","DOI":"10.1145\/2896338.2896349"},{"key":"263_CR41","unstructured":"Ruder, S.: \u201cAn overview of multi-task learning in deep neural networks,\u201d arXiv preprint arXiv:1706.05098, (2017)"},{"key":"263_CR42","doi-asserted-by":"crossref","unstructured":"Song, C., Ristenpart, T., and Shmatikov, V.: \u201cMachine learning models that remember too much,\u201d in ACM CCS, (2017)","DOI":"10.1145\/3133956.3134077"},{"key":"263_CR43","doi-asserted-by":"crossref","unstructured":"Phan, N., Wang, Y., Wu, X., and Dou, D.: \u201cDifferential privacy preservation for deep auto-encoders: an application of human behavior prediction,\u201d in AAAI, (2016)","DOI":"10.1609\/aaai.v30i1.10165"},{"key":"263_CR44","unstructured":"Xie, L., Lin, K., Wang, S., Wang, F., and Zhou, J.: \u201cDifferentially private generative adversarial network,\u201d CoRR, (2018)"},{"key":"263_CR45","doi-asserted-by":"crossref","unstructured":"Duchi, J.C., Jordan, M.I., and Wainwright, M.J.: \u201cLocal privacy and statistical minimax rates,\u201d in IEEE FOCS, (2013)","DOI":"10.1109\/FOCS.2013.53"},{"key":"263_CR46","unstructured":"Settles, B.: Active learning literature survey. University of Wisconsin-Madison Department of Computer Sciences, Tech. Rep. (2009)"},{"key":"263_CR47","doi-asserted-by":"crossref","unstructured":"Du, W., and Wu, X.: \u201cAdvpl: Adversarial personalized learning,\u201d in DSAA, (2020)","DOI":"10.1109\/DSAA49011.2020.00021"}],"container-title":["International Journal of Data Science and Analytics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s41060-021-00263-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s41060-021-00263-3\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s41060-021-00263-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,12,27]],"date-time":"2022-12-27T15:13:49Z","timestamp":1672154029000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s41060-021-00263-3"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,5,17]]},"references-count":47,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2021,6]]}},"alternative-id":["263"],"URL":"https:\/\/doi.org\/10.1007\/s41060-021-00263-3","relation":{},"ISSN":["2364-415X","2364-4168"],"issn-type":[{"type":"print","value":"2364-415X"},{"type":"electronic","value":"2364-4168"}],"subject":[],"published":{"date-parts":[[2021,5,17]]},"assertion":[{"value":"17 November 2020","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"6 May 2021","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"17 May 2021","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"On behalf of all authors, the corresponding author states that there is no conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}