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arXiv · 2603.00502

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

Wenhao Zheng·Wang Lu·Fangshuang Tang·Yiyang Lu·Jun Yang·Pengcheng Xiong·Yulan Yan
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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.

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Wenhao Zheng, Wang Lu, Fangshuang Tang, Yiyang Lu, Jun Yang, Pengcheng Xiong, Yulan Yan. 2026-02-28. Trinity: A Scenario-Aware Recommendation Framework for Large-Scale Cold-Start Users. https://arxiv.org/abs/2603.00502

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