{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T01:34:51Z","timestamp":1760060091594,"version":"build-2065373602"},"reference-count":69,"publisher":"MDPI AG","issue":"8","license":[{"start":{"date-parts":[[2025,8,11]],"date-time":"2025-08-11T00:00:00Z","timestamp":1754870400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Natural Science Foundation of China","award":["72231010","71932008","72362004","qiankehejichu[2024]Youth183","qiankehejichu-ZK[2022]yiban019","qiankehezhichengDXGA[2025]yiban014","2020YJ045"],"award-info":[{"award-number":["72231010","71932008","72362004","qiankehejichu[2024]Youth183","qiankehejichu-ZK[2022]yiban019","qiankehezhichengDXGA[2025]yiban014","2020YJ045"]}]},{"name":"Guizhou Provincial Science and Technology Projects","award":["72231010","71932008","72362004","qiankehejichu[2024]Youth183","qiankehejichu-ZK[2022]yiban019","qiankehezhichengDXGA[2025]yiban014","2020YJ045"],"award-info":[{"award-number":["72231010","71932008","72362004","qiankehejichu[2024]Youth183","qiankehejichu-ZK[2022]yiban019","qiankehezhichengDXGA[2025]yiban014","2020YJ045"]}]},{"name":"Research Start-up Project for Recruited Talents of Guizhou University of Finance and Economics [2022]","award":["72231010","71932008","72362004","qiankehejichu[2024]Youth183","qiankehejichu-ZK[2022]yiban019","qiankehezhichengDXGA[2025]yiban014","2020YJ045"],"award-info":[{"award-number":["72231010","71932008","72362004","qiankehejichu[2024]Youth183","qiankehejichu-ZK[2022]yiban019","qiankehezhichengDXGA[2025]yiban014","2020YJ045"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Systems"],"abstract":"<jats:p>On food delivery platforms, user decisions are often driven by dynamic contextual factors such as time, intent, and lifestyle patterns. Traditional context-aware recommender systems struggle to capture such implicit signals, especially when user behavior spans heterogeneous long- and short-term patterns. To address this, we propose a context-driven recommendation framework that integrates a hybrid sequence modeling architecture with a Large Language Model for post hoc reasoning and reranking. Specifically, the solution tackles several key issues: (1) integration of multimodal features to achieve explicit context fusion through a hybrid fusion strategy; (2) introduction of a context capture layer and a context propagation layer to enable effective encoding of implicit contextual states hidden in the heterogeneous long and short term; (3) cross attention mechanisms to facilitate context retrospection, which allows implicit contexts to be uncovered; and (4) leveraging the reasoning capabilities of DeepSeek-R1 as a post-processing step to perform open knowledge-enhanced reranking. Extensive experiments on a real-world dataset show that our approach significantly outperforms strong baselines in both prediction accuracy and Top-K recommendation quality. Case studies further demonstrate the model\u2019s ability to uncover nuanced, implicit contextual cues\u2014such as family roles and holiday-specific behaviors\u2014making it particularly effective for personalized, dynamic recommendations in high-frequency scenes.<\/jats:p>","DOI":"10.3390\/systems13080682","type":"journal-article","created":{"date-parts":[[2025,8,11]],"date-time":"2025-08-11T14:32:36Z","timestamp":1754922756000},"page":"682","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Context-Driven Recommendation via Heterogeneous Temporal Modeling and Large Language Model in the Takeout System"],"prefix":"10.3390","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9781-3101","authenticated-orcid":false,"given":"Wei","family":"Deng","sequence":"first","affiliation":[{"name":"School of Big Data Statistics, Guizhou University of Finance and Economics, Guiyang 550025, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0001-1494-1819","authenticated-orcid":false,"given":"Dongyi","family":"Hu","sequence":"additional","affiliation":[{"name":"School of Big Data Statistics, Guizhou University of Finance and Economics, Guiyang 550025, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zilong","family":"Jiang","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Technology, Guizhou University of Finance and Economics, Guiyang 550025, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Peng","family":"Zhang","sequence":"additional","affiliation":[{"name":"Cyberspace Institute of Advanced Technology, Guangzhou University, Guangzhou 510006, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yong","family":"Shi","sequence":"additional","affiliation":[{"name":"University of Chinese Academy of Sciences, Beijing 100190, China"},{"name":"The Key Laboratory of Big Data Mining and Knowledge Management, Chinese Academy of Sciences, Beijing 100190, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2025,8,11]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Wang, S., Hu, L., Wang, Y., Cao, L., Sheng, Q.Z., and Orgun, M. 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