AgentTailor: Dual-Gate LLM-Assisted Re-ranking for Personalized Long-Tail Recommendation

Recommender systems are central to modern digital platforms but remain vulnerable to popularity bias, where head items dominate exposure while many long-tail items are underrepresented. Existing long-tail methods often rely on global reweighting or opaque ranking models, making it difficult to personalize tail exposure without degrading recommendation utility. To address this challenge, we propose AgentTailor, an interpretable LLM-assisted framework for personalized long-tail recommendation. AgentTailor decomposes recommendation into two complementary stages. First, a semantic recall agent retrieves a semantically aligned candidate pool, determining whether relevant long-tail items are accessible to later ranking. Second, a conditional tail-specialist branch performs evidence-based re-ranking through a dual-gate tail-pressure router: the activation gate decides whether LLM-assisted evidence assessment should be invoked, while the placement gate decides whether the extracted evidence is allowed to modify the final ranking. Activated users receive item-level relevance and tail-fit evidence for routed candidates, but the final list is changed only when stable placement is warranted; otherwise, the Stage 1 order is preserved. Thus, the LLM provides auditable evidence rather than freely generating the final ranking. Experiments on MovieLens-1M and BookCrossing show that AgentTailor improves long-tail recommendation while maintaining competitive overall performance under a controlled LLM re-ranking protocol. Further analyses demonstrate its cross-backbone generalization, reliable structured outputs, and controllable trade-off between relevance preservation and long-tail exposure. The code and data are available at https://anonymous.4open.science/r/AgentTailor-CAEF.