Cross-Domain Interest Representation Learning for Scenario- and Task-Aware Recommendation

Abstract

Many internet companies operate multiple flagship applications, each of which can be regarded as a distinct business domain, covering areas such as video, reading, and gaming. Within each domain, diverse recommendation scenarios coexist, and users engage in various tasks with heterogeneous behaviors. In our industrial setting, we observe three key phenomena that existing methods rarely address: (i) users' Cross-Domain Interests (CDI) are weakly exploited, since behavior sequences are often pooled for efficiency, losing transferable cross-domain dependencies; (ii) multi-domain, scenario, and task variations are under-modeled, making it difficult to capture fine-grained complementarities; and (iii) multimodal features remain misaligned with ID features, especially when different domains emphasize different modalities. These gaps hinder cross-product collaboration. We propose STAR-CDR, a Scenario- and Task-Aware Cross-Domain Recommendation architecture. Following the modular decomposition of industrial recommenders, STAR-CDR introduces three innovations: (i) a CDI Extractor that enhances sequential modeling with domain indicators and other key features; (ii) a CDI Injector that recalibrates activations and enables domain-/scenario-/task-aware expert routing; and (iii) a CDI Multimodal Adapter that aligns image, text, and ID features under CDI-guided gating. Compared to the strongest baseline in our setting, STAR-CDR improves offline AUC 3.5% across five domains and yields +3.17% to +9.24% relative KPI gains in online A/B tests. The system now supports multiple domains (flagship applications), scenarios (recommendation scenarios), and tasks (user behaviors) at scale, serving over 280 million daily active users. We provide a GitHub repository. https://github.com/Rescomk/STAR-CDR with STAR-CDR's code, training scripts, and a synthetic data construction pipeline.