{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,9]],"date-time":"2025-10-09T21:12:08Z","timestamp":1760044328583,"version":"3.37.3"},"reference-count":54,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","issue":"10","license":[{"start":{"date-parts":[[2017,10,1]],"date-time":"2017-10-01T00:00:00Z","timestamp":1506816000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2017,10,1]],"date-time":"2017-10-01T00:00:00Z","timestamp":1506816000000},"content-version":"am","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2017,10,1]],"date-time":"2017-10-01T00:00:00Z","timestamp":1506816000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2017,10,1]],"date-time":"2017-10-01T00:00:00Z","timestamp":1506816000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"funder":[{"DOI":"10.13039\/100000001","name":"US National Science Foundation","doi-asserted-by":"publisher","award":["IIS-1657196"],"award-info":[{"award-number":["IIS-1657196"]}],"id":[{"id":"10.13039\/100000001","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000001","name":"US National Science Foundation","doi-asserted-by":"publisher","award":["IIS-1650723"],"award-info":[{"award-number":["IIS-1650723"]}],"id":[{"id":"10.13039\/100000001","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Trans. Knowl. Data Eng."],"published-print":{"date-parts":[[2017,10,1]]},"DOI":"10.1109\/tkde.2017.2722411","type":"journal-article","created":{"date-parts":[[2017,7,3]],"date-time":"2017-07-03T18:09:35Z","timestamp":1499105375000},"page":"2360-2373","source":"Crossref","is-referenced-by-count":9,"title":["Wellness Representation of Users in Social Media: Towards Joint Modelling of Heterogeneity and Temporality"],"prefix":"10.1109","volume":"29","author":[{"given":"Mohammad","family":"Akbari","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xia","family":"Hu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Fei","family":"Wang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Tat-Seng","family":"Chua","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"263","reference":[{"key":"ref39","doi-asserted-by":"publisher","DOI":"10.1145\/2600428.2611185"},{"key":"ref38","first-page":"177","article-title":"Predicting user&#x2019;s political party using ideological stances","author":"gottipati","year":"2013","journal-title":"Proc Int Conf Inf Soc"},{"key":"ref33","doi-asserted-by":"publisher","DOI":"10.3115\/1118693.1118721"},{"key":"ref32","doi-asserted-by":"publisher","DOI":"10.1145\/2433396.2433465"},{"key":"ref31","article-title":"Introductory lectures on convex optimization: A basic course","volume":"87","author":"nesterov","year":"2013"},{"key":"ref30","first-page":"746","article-title":"Accelerated gradient method for multi-task sparse learning problem","author":"chen","year":"2009","journal-title":"Proc IEEE Int Conf Data Mining"},{"key":"ref37","doi-asserted-by":"publisher","DOI":"10.1145\/2671188.2749381"},{"key":"ref36","doi-asserted-by":"publisher","DOI":"10.1016\/j.jbi.2005.07.002"},{"key":"ref35","doi-asserted-by":"publisher","DOI":"10.1197\/jamia.M3378"},{"key":"ref34","first-page":"17","article-title":"Effective mapping of biomedical text to the UMLS metathesaurus: The metamap program","author":"aronson","year":"2001","journal-title":"Proc AMIA Symp"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.1109\/TASL.2006.885253"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.1056\/NEJMp1114866"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.1145\/2733373.2806217"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1145\/2556288.2557214"},{"key":"ref1","first-page":"87","article-title":"From tweets to wellness: Wellness event detection from twitter streams","author":"akbari","year":"2016","journal-title":"Proc AAAI Conf Artif Intell"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1145\/2661829.2661998"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1145\/2566486.2567975"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1145\/2806416.2806511"},{"key":"ref24","first-page":"556","article-title":"Algorithms for non-negative matrix factorization","author":"lee","year":"2001","journal-title":"Proc Int Conf Neural Inf Process"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1145\/2783258.2783324"},{"key":"ref26","first-page":"964","article-title":"A dirty model for multi-task learning","author":"jalali","year":"2010","journal-title":"Proc Int Conf Neural Inf Process"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1145\/2600428.2609568"},{"key":"ref50","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2011.217"},{"key":"ref51","doi-asserted-by":"publisher","DOI":"10.1145\/2339530.2339732"},{"key":"ref54","first-page":"446","article-title":"A multivariate timeseries modeling approach to severity of illness assessment and forecasting in ICU with sparse, heterogeneous clinical data","author":"ghassemi","year":"2015","journal-title":"Proc Innovative Applicat Artif Intell"},{"key":"ref53","doi-asserted-by":"publisher","DOI":"10.1145\/1772690.1772773"},{"key":"ref52","first-page":"2691","article-title":"GBPR: Group preference based Bayesian personalized ranking for one-class collaborative filtering","author":"pan","year":"2013","journal-title":"Proc Int Joint Conf Artif Intell"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2012.111"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1145\/2009916.2010004"},{"key":"ref40","first-page":"37","article-title":"Evaluation: From precision, recall and F-measure to ROC, informedness, markedness and correlation","volume":"2","author":"powers","year":"2011","journal-title":"J Mach Learn Technol"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1145\/2766462.2767726"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1145\/2783258.2783308"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1109\/ICPR.2014.476"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1145\/2623330.2623754"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1016\/j.neuroimage.2013.03.073"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1145\/2623330.2623755"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1145\/2806416.2806644"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1145\/2507157.2507209"},{"key":"ref4","first-page":"540","article-title":"Insights from machine-learned diet success prediction","author":"weber","year":"2015","journal-title":"Proc Pacific Symp Biocomput"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1145\/2647868.2654945"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1136\/amiajnl-2013-002151"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1016\/S0140-6736(15)61428-3"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1145\/2783258.2783403"},{"key":"ref49","doi-asserted-by":"crossref","first-page":"1548","DOI":"10.1109\/TPAMI.2010.231","article-title":"Graph regularized nonnegative matrix factorization for data representation","volume":"33","author":"cai","year":"2011","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1145\/2783258.2783352"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1145\/2623330.2623711"},{"key":"ref46","doi-asserted-by":"publisher","DOI":"10.1145\/2766462.2767718"},{"key":"ref45","doi-asserted-by":"publisher","DOI":"10.1145\/3018661.3018693"},{"key":"ref48","doi-asserted-by":"publisher","DOI":"10.1145\/2766462.2767716"},{"key":"ref47","doi-asserted-by":"publisher","DOI":"10.1145\/1571941.1571966"},{"key":"ref42","first-page":"507","article-title":"Laplacian score for feature selection","author":"he","year":"2005","journal-title":"Proc Int Conf Neural Inf Process"},{"key":"ref41","doi-asserted-by":"publisher","DOI":"10.1145\/2408736.2408740"},{"key":"ref44","first-page":"1026","article-title":"Unsupervised feature selection using nonnegative spectral analysis","author":"li","year":"2012","journal-title":"Proc Innovative Applicat Artif Intell"},{"key":"ref43","doi-asserted-by":"publisher","DOI":"10.1145\/1273496.1273641"}],"container-title":["IEEE Transactions on Knowledge and Data Engineering"],"original-title":[],"link":[{"URL":"http:\/\/ieeexplore.ieee.org\/ielaam\/69\/8031002\/7967681-aam.pdf","content-type":"application\/pdf","content-version":"am","intended-application":"syndication"},{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/69\/8031002\/07967681.pdf?arnumber=7967681","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,4,8]],"date-time":"2022-04-08T18:49:04Z","timestamp":1649443744000},"score":1,"resource":{"primary":{"URL":"http:\/\/ieeexplore.ieee.org\/document\/7967681\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2017,10,1]]},"references-count":54,"journal-issue":{"issue":"10"},"URL":"https:\/\/doi.org\/10.1109\/tkde.2017.2722411","relation":{},"ISSN":["1041-4347"],"issn-type":[{"type":"print","value":"1041-4347"}],"subject":[],"published":{"date-parts":[[2017,10,1]]}}}