PREX-Rec: Provenance-Aware Experience Transfer for Sequential Recommendation

Reusing a recommendation experience across users requires deciding whether its guidance fits the target user's behavior. Textual similarity alone does not explicitly encode the source interactions associated with that guidance. We propose PREX-Rec, which pairs each natural-language experience with LLM-selected items from its source history and uses these associations for retrieval. A user-item-experience relational index connects target histories to experiences through shared items, weighted by evidence frequency and recency. An LLM screens retrieved experiences for applicability; independent experience-guided rankings are then fused with a base ranking. On the evaluated Amazon Games and Arts candidate-ranking tasks, PREX-Rec improves all reported metrics over five LLM-based baselines. Games NDCG@5 increases from the strongest baseline's 0.2918 to 0.3291. With independent integration fixed, item-based retrieval improves over the evaluated vector retriever; perturbing experience-item assignments within source histories lowers Games NDCG@5 to 0.3086. These results support the utility of experience-specific item associations within the evaluated retrieval-and-ranking framework.