In e-commerce, Trigger-Induced Recommendation (TIR), recommending items after a user clicks a trigger, is an important task. However, modern platforms rely on a continuous stream of diverse and short-lived promotional scenarios (e.g., for Black Friday), creating a significant challenge. Existing methods are less effective here: they either fall into a trigger-dependency trap, recommending overly similar items, or a data-hungry trap, requiring long-term stable data for intent modeling that these ephemeral scenarios cannot provide. To address these limitations, we propose the Collaborative Contrastive Network (CCN), a general and robust framework that approaches the problem from a different perspective. Instead of modeling ambiguous entry intent, CCN learns a user's context-specific preferences by treating the user-trigger pair as a unique condition. It achieves this via a novel contrastive learning scheme, using the collaborative feedback of co-click/co-non-click as a positive signal and mono-click as a negative signal to structure the item representation latent space. To prove its real-world generality, CCN is trained on a heterogeneous dataset spanning over a dozen different scenarios from an entire year, and the online A/B test is conducted in a completely new, unseen scenario on Taobao, where CCN boosts CTR by 12.3\% and order volume by 12.7\%, demonstrating its effectiveness and generalization.

翻译：在电商领域，触发型推荐（TIR）是一项重要任务，即用户点击触发项后为其推荐商品。然而，现代平台依赖持续涌入的多样化、短周期促销场景（如黑色星期五），这带来了显著挑战。现有方法在此类场景中效果不佳：它们要么陷入触发依赖陷阱，推荐过于相似的商品；要么陷入数据饥饿陷阱，需要长期稳定的数据进行意图建模，而这些短暂场景无法提供此类数据。为解决这些局限，我们提出协作对比网络（CCN）——一个从全新视角出发的通用鲁棒框架。CCN不建模模糊的入口意图，而是将用户-触发项对视为独特条件，学习用户的情境特定偏好。它通过创新的对比学习机制实现此目标：将协点击/协非点击的协作反馈作为正信号，将单点击作为负信号，以结构化物品表征的隐空间。为证明其现实泛化性，CCN在覆盖全年十余种不同场景的异构数据集上训练，并在淘宝一个全新未见场景中开展在线A/B测试。在该测试中，CCN将点击率提升12.3%，订单量提升12.7%，验证了其有效性与泛化能力。