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

2026-02-28

AI Summary

This work addresses the degradation in recommendation performance for cold-start users in new scenarios, which stems from sparse user behavior, low engagement, and model instability. To tackle this challenge, the authors propose an end-to-end scene-aware recommendation framework that jointly designs feature engineering, model architecture, and a stable online updating mechanism. By leveraging cross-scenario feature extraction and knowledge transfer, the framework enables effective modeling of new users in novel contexts. Evaluated on a billion-scale user product migration task at Microsoft, the approach demonstrates significant improvements over existing methods in both offline and online experiments, substantially enhancing the accuracy and robustness of cold-start recommendations.

Technology Category

Data Mining & Knowledge Management: Recommender SystemsMachine Learning: Hardware-aware MLComputer Vision: Scene Analysis & Understanding

Application Category

User Modeling, Personalization and Recommendation: Attacks and countermeasures in recommendation systemsSearch and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for rankingEconomics, Online Markets and Human Computation: Economics and fairness of platforms and recommendation systems

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.

Problem

• cold-start
• new scenario
• large-scale users
• recommendation
• user behavior sparsity

Innovation

• cold-start recommendation
• scenario-aware framework
• feature engineering
• model architecture
• stable model updating