{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,18]],"date-time":"2026-03-18T02:56:33Z","timestamp":1773802593841,"version":"3.50.1"},"reference-count":0,"publisher":"Association for the Advancement of Artificial Intelligence (AAAI)","issue":"18","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["AAAI"],"abstract":"<jats:p>Urban region embedding, which learns dense vector representations for urban zones, plays a foundational role in data-driven urban intelligence. These representations are critical for downstream applications like public safety management\nand infrastructure development, requiring nuanced understanding of urban functionality. A core challenge remains effective fusion of multi-view data (e.g., human mobility flows and static regional attributes) into unified zone representations. To this end, we propose MVJC, a Multi-view Joint Learning and Contrastive Learning framework, which employs: (1) Multi-view Joint Learning (MVJL) layer to model intra-view dependencies to extract view-specific features and (2) Multi-view Contrastive Learning (MVCL) layer to perform cross-region aggregation to derive consensus representations while capturing the regional complementarity. We further introduce a structure-aware contrastive loss that mitigates false negatives by aligning representations through region topology instead of instance identity. Extensive experiments on New York City datasets demonstrate MVJC\u2019s superiority: it reduces crime prediction MAE by 9.1% (vs. 66.9 baseline) and improves land use clustering F-measure by 55.6% (vs. 0.45 baseline) over state-of-the-art method, which is attributed to MVJC\u2019s synergy of joint and contrastive learning, yielding representations that are simultaneously predictive and semantically discriminative.<\/jats:p>","DOI":"10.1609\/aaai.v40i18.38551","type":"journal-article","created":{"date-parts":[[2026,3,18]],"date-time":"2026-03-18T00:37:15Z","timestamp":1773794235000},"page":"15261-15268","source":"Crossref","is-referenced-by-count":0,"title":["Comprehensive Urban Region Representation Learning via Multi-View Joint Learning and Contrastive Learning"],"prefix":"10.1609","volume":"40","author":[{"given":"Yingde","family":"Lin","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yuanbo","family":"Xu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Lu","family":"Jiang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Pengyang","family":"Wang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"9382","published-online":{"date-parts":[[2026,3,14]]},"container-title":["Proceedings of the AAAI Conference on Artificial Intelligence"],"original-title":[],"link":[{"URL":"https:\/\/ojs.aaai.org\/index.php\/AAAI\/article\/download\/38551\/42513","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/ojs.aaai.org\/index.php\/AAAI\/article\/download\/38551\/42513","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,3,18]],"date-time":"2026-03-18T00:37:15Z","timestamp":1773794235000},"score":1,"resource":{"primary":{"URL":"https:\/\/ojs.aaai.org\/index.php\/AAAI\/article\/view\/38551"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,3,14]]},"references-count":0,"journal-issue":{"issue":"18","published-online":{"date-parts":[[2026,3,17]]}},"URL":"https:\/\/doi.org\/10.1609\/aaai.v40i18.38551","relation":{},"ISSN":["2374-3468","2159-5399"],"issn-type":[{"value":"2374-3468","type":"electronic"},{"value":"2159-5399","type":"print"}],"subject":[],"published":{"date-parts":[[2026,3,14]]}}}