Scenario-Aware Multi-Interest Retrieval with Hierarchical Language Models

Feed, search, and advertising capture different aspects of user interests, but their interaction histories differ substantially in frequency and temporal structure. Combining these heterogeneous signals requires deciding both which events enter the model and how they are represented for retrieval. We present RED-Rec (Recommender Engine for Diversified Scenarios), a hierarchical language-model framework for multi-scenario recommendation. RED-Rec first employs a scenario-aware 2-D mixing policy that combines per-scenario history quotas with blockwise temporal ordering. Learnable interest queries then extract multiple user representations from the mixed sequence. During training, an item-aware gate distributes supervision across queries; at serving time, each query independently retrieves candidates from precomputed item embeddings, and the retrieved results are merged by maximum similarity. This design enables cross-scenario interest modeling while preserving efficient two-tower retrieval. Controlled experiments isolate the effects of the mixer and interest queries on a common backbone, while matched-budget comparisons demonstrate improvements over adapted multi-interest readouts. Experiments on public benchmarks and an industrial dataset show consistent gains in retrieval accuracy, with the largest cross-scenario improvements observed for advertising. In a one-week online A/B test, RED-Rec increases advertiser value by 0.8864% and advertising spend by 0.3401%.