[2603.00502] Trinity: A Scenario-Aware Recommendation Framework for Large-Scale Cold-Start Users

Computer Science > Machine Learning arXiv:2603.00502 (cs) [Submitted on 28 Feb 2026] Title:Trinity: A Scenario-Aware Recommendation Framework for Large-Scale Cold-Start Users Authors:Wenhao Zheng, Wang Lu, Fangshuang Tang, Yiyang Lu, Jun Yang, Pengcheng Xiong, Yulan Yan View a PDF of the paper titled Trinity: A Scenario-Aware Recommendation Framework for Large-Scale Cold-Start Users, by Wenhao Zheng and 6 other authors View PDF HTML (experimental) Abstract:Early-stage users in a new scenario intensify cold-start challenges, yet prior works often address only parts of the problem through model architecture. Launching a new user experience to replace an established product involves sparse behavioral signals, low-engagement cohorts, and unstable model performance. We argue that effective recommendations require the synergistic integration of feature engineering, model architecture, and stable model updating. We propose Trinity, a framework embodying this principle. Trinity extracts valuable information from existing scenarios while ensuring predictive effectiveness and accuracy in the new scenario. In this paper, we showcase Trinity applied to a billion-user Microsoft product transition. Both offline and online experiments demonstrate that our framework achieves substantial improvements in addressing the combined challenge of new users in new scenarios. Subjects: Machine Learning (cs.LG) Cite as: arXiv:2603.00502 [cs.LG]   (or arXiv:2603.00502v1 [cs.LG] for this version)   ht...