RouteRec: Strict Evaluation of Recommender-Agent Selection and Aggregation
Recommender systems increasingly face a choice among heterogeneous agents -- collaborative filters, sequential models, content-based retrievers, and LLM-based rerankers -- yet no single agent is uniformly best. We study this choice as task-aware agent ranking under cost constraints using RouteRec, a framework that compares request-level hard selection with item-level learned aggregation over four traditional recommender agents and one LLM reranker agent. On MovieLens-1M, the full quality oracle has substantial headroom (HR@10 = 0.584), confirming that useful cross-agent signal exists. Under a leakage-free 5-fold out-of-fold protocol, however, hard selection remains below BM25 (0.223 vs. 0...
| Authors | Kaiji Zhou, Vladimir Kalmykov, Yue Feng |
|---|---|
| Primary category | cs.CL |
| Published | 10 July 2026 |
| Last updated at source | 10 July 2026 |
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