RouteRec: Strict Evaluation of Recommender-Agent Selection and Aggregation
RouteRec evaluates task-aware agent selection and aggregation to optimize recommender system performance under cost constraints.
The RouteRec framework compares request-level hard selection against item-level learned aggregation across heterogeneous agents, including collaborative filters and LLM-based rerankers. Testing on MovieLens-1M demonstrates significant headroom for performance gains through cross-agent signal utilization.