{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,17]],"date-time":"2026-03-17T19:25:50Z","timestamp":1773775550347,"version":"3.50.1"},"publisher-location":"California","reference-count":0,"publisher":"International Joint Conferences on Artificial Intelligence Organization","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2019,8]]},"abstract":"<jats:p>Automated representation  learning is  behind many recent success stories in machine learning. It is often used to transfer knowledge learned from a large dataset (e.g., raw text) to tasks for which only a small number of training examples are available.  In this paper, we review recent advance in learning to represent social media users in low-dimensional embeddings.   The  technology  is  critical  for creating high performance social media-based human traits and behavior models since the ground truth for assessing latent human traits and behavior is often expensive to acquire at a large scale.  In this survey, we review typical methods for learning a unified user embeddings from heterogeneous user data  (e.g., combines social media texts with images to learn a unified user representation).  Finally we point out some current issues and future directions.<\/jats:p>","DOI":"10.24963\/ijcai.2019\/881","type":"proceedings-article","created":{"date-parts":[[2019,7,28]],"date-time":"2019-07-28T03:46:05Z","timestamp":1564285565000},"page":"6318-6324","source":"Crossref","is-referenced-by-count":18,"title":["Social Media-based User Embedding: A Literature Review"],"prefix":"10.24963","author":[{"given":"Shimei","family":"Pan","sequence":"first","affiliation":[{"name":"Department of Information Systems, University of Maryland, Baltimore County"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Tao","family":"Ding","sequence":"additional","affiliation":[{"name":"Department of Information Systems, University of Maryland, Baltimore County"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"10584","event":{"name":"Twenty-Eighth International Joint Conference on Artificial Intelligence {IJCAI-19}","theme":"Artificial Intelligence","location":"Macao, China","acronym":"IJCAI-2019","number":"28","sponsor":["International Joint Conferences on Artificial Intelligence Organization (IJCAI)"],"start":{"date-parts":[[2019,8,10]]},"end":{"date-parts":[[2019,8,16]]}},"container-title":["Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence"],"original-title":[],"deposited":{"date-parts":[[2019,7,28]],"date-time":"2019-07-28T03:52:26Z","timestamp":1564285946000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.ijcai.org\/proceedings\/2019\/881"}},"subtitle":[],"proceedings-subject":"Artificial Intelligence Research Articles","short-title":[],"issued":{"date-parts":[[2019,8]]},"references-count":0,"URL":"https:\/\/doi.org\/10.24963\/ijcai.2019\/881","relation":{},"subject":[],"published":{"date-parts":[[2019,8]]}}}