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

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.