{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,26]],"date-time":"2025-12-26T07:03:58Z","timestamp":1766732638475,"version":"3.40.4"},"reference-count":0,"publisher":"Association for the Advancement of Artificial Intelligence (AAAI)","issue":"27","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["AAAI"],"abstract":"<jats:p>Estimating service capabilities for logistics terminal stations is essential for guiding operations adjustments to enhance customer experience.  \nHowever, existing studies often focus on isolated metrics like on-time delivery or complaint rates, each reflecting a specific aspect of service capabilities. \nTo provide a more comprehensive evaluation, we design AdaService, an Adaptive multi-faceted Service capabilities co-estimation framework.  \nWe begin by constructing Multi-faceted Hypergraph to encode stations using multiple performance metrics.  \nWe then introduce a Multi-faceted Hypergraph Convolution Network (MHCN) to capture the heterogeneous service capabilities across stations, providing a comprehensive capabilities representation. \nFinally, we apply an Adaptive Multi-faceted Estimation module that uses multi-task learning to model dynamic interactions among these metrics, enhancing predictive accuracy.\nExtensive evaluation with real-world data collected from nationwide stations in a leading logistics company in China demonstrates that AdaService significantly outperforms state-of-the-art methods, improving estimation accuracy for on-time delivery, on-time pick-up, and complaint rates by up to 18.98%, 9.30%, and 39.62%.<\/jats:p>","DOI":"10.1609\/aaai.v39i27.35079","type":"journal-article","created":{"date-parts":[[2025,4,11]],"date-time":"2025-04-11T14:33:52Z","timestamp":1744382032000},"page":"28557-28565","source":"Crossref","is-referenced-by-count":1,"title":["Adaptive Multi-Faceted Service Capabilities Co-Prediction for Nationwide Terminal Stations in Logistics"],"prefix":"10.1609","volume":"39","author":[{"given":"Shuxin","family":"Zhong","sequence":"first","affiliation":[]},{"given":"Kimberly","family":"Liu","sequence":"additional","affiliation":[]},{"given":"Wenjun","family":"Lyu","sequence":"additional","affiliation":[]},{"given":"Haotian","family":"Wang","sequence":"additional","affiliation":[]},{"given":"Guang","family":"Wang","sequence":"additional","affiliation":[]},{"given":"Yunhuai","family":"Liu","sequence":"additional","affiliation":[]},{"given":"Tian","family":"He","sequence":"additional","affiliation":[]},{"given":"Yu","family":"Yang","sequence":"additional","affiliation":[]},{"given":"Desheng","family":"Zhang","sequence":"additional","affiliation":[]}],"member":"9382","published-online":{"date-parts":[[2025,4,11]]},"container-title":["Proceedings of the AAAI Conference on Artificial Intelligence"],"original-title":[],"link":[{"URL":"https:\/\/ojs.aaai.org\/index.php\/AAAI\/article\/download\/35079\/37234","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/ojs.aaai.org\/index.php\/AAAI\/article\/download\/35079\/37234","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,4,11]],"date-time":"2025-04-11T14:33:52Z","timestamp":1744382032000},"score":1,"resource":{"primary":{"URL":"https:\/\/ojs.aaai.org\/index.php\/AAAI\/article\/view\/35079"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,4,11]]},"references-count":0,"journal-issue":{"issue":"27","published-online":{"date-parts":[[2025,4,11]]}},"URL":"https:\/\/doi.org\/10.1609\/aaai.v39i27.35079","relation":{},"ISSN":["2374-3468","2159-5399"],"issn-type":[{"value":"2374-3468","type":"electronic"},{"value":"2159-5399","type":"print"}],"subject":[],"published":{"date-parts":[[2025,4,11]]}}}