Beyond the Trigger: Learning Collaborative Context for Generalizable Trigger-Induced Recommendation

TL;DR

This paper introduces CCN, a contrastive learning framework that improves trigger-induced recommendation in e-commerce by capturing user context-specific preferences, demonstrating strong generalization across diverse scenarios.

Contribution

The paper proposes a novel contrastive learning approach, CCN, that models user-trigger pairs for better generalization in ephemeral promotional scenarios.

Findings

CCN boosts CTR by 12.3% in online A/B tests.

CCN increases order volume by 12.7%.

Effective across over a dozen diverse scenarios.

Abstract

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.