离线推理用于高效推荐：基于LLM的个性化-属性化物品索引
Offline Reasoning for Efficient Recommendation: LLM-Empowered Persona-Profiled Item Indexing
AI总结：
Persona4Rec通过离线推理构建物品个性化表示，实现高效且可解释的推荐系统，减少推理时间并提供直观解释。
摘要：
近年来，大语言模型（LLM）的进步为推荐系统带来了新的机遇，通过丰富的语义理解和上下文推理来捕捉用户兴趣和物品特征的细微语义。特别是，LLM已被用作重排序器，通过推断用户-物品相关性来重新排序候选物品。然而，这些方法往往需要昂贵的在线推理时间推理，导致高延迟，阻碍了实际应用。在本工作中，我们介绍了Persona4Rec，一个推荐框架，通过离线推理来构建可解释的物品个性化表示，使实时推理变得轻量且可扩展。在离线阶段，Persona4Rec利用LLM对物品评论进行推理，推断出不同类型的用户可能与物品互动的不同动机；这些推断出的动机被转化为个性化表示，为每个物品提供多种可解释的视角。与传统方法仅依赖单一物品表示不同，Persona4Rec通过专用编码器学习将用户资料与最可能的物品侧个性化进行对齐，有效地将用户-物品相关性转化为用户-个性化相关性。在在线阶段，这种个性化-属性化物品索引允许快速的相关性计算，而无需调用昂贵的LLM推理。广泛的实验表明，Persona4Rec在性能上与最近的基于LLM的重排序器相当，同时显著减少了推理时间。此外，定性分析证实，个性化表示不仅推动了高效的评分，还提供了直观的、基于评论的解释。这些结果表明，Persona4Rec为下一代推荐系统提供了一个实用且可解释的解决方案。
英文摘要：
Recent advances in large language models (LLMs) offer new opportunities for recommender systems by capturing the nuanced semantics of user interests and item characteristics through rich semantic understanding and contextual reasoning. In particular, LLMs have been employed as rerankers that reorder candidate items based on inferred user-item relevance. However, these approaches often require expensive online inference-time reasoning, leading to high latency that hampers real-world deployment. In this work, we introduce Persona4Rec, a recommendation framework that performs offline reasoning to construct interpretable persona representations of items, enabling lightweight and scalable real-time inference. In the offline stage, Persona4Rec leverages LLMs to reason over item reviews, inferring diverse user motivations that explain why different types of users may engage with an item; these inferred motivations are materialized as persona representations, providing multiple, human-interpretable views of each item. Unlike conventional approaches that rely on a single item representation, Persona4Rec learns to align user profiles with the most plausible item-side persona through a dedicated encoder, effectively transforming user-item relevance into user-persona relevance. At the online stage, this persona-profiled item index allows fast relevance computation without invoking expensive LLM reasoning. Extensive experiments show that Persona4Rec achieves performance comparable to recent LLM-based rerankers while substantially reducing inference time. Moreover, qualitative analysis confirms that persona representations not only drive efficient scoring but also provide intuitive, review-grounded explanations. These results demonstrate that Persona4Rec offers a practical and interpretable solution for next-generation recommender systems.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
