用上下文感知的物品画像提升大模型推荐效果
Profiling What Matters: Context-Aware Item Profiles from Large-Scale Metadata for LLM Recommenders
- 将物品元数据拆分为客观特征与主观特质，动态筛选相关描述
- 在有限推理开销下实现个性化物品画像，显著提升推荐精度
- 适合需要精细理解物品信息的大模型推荐系统开发者
尽管大语言模型（LLMs）在推荐系统重排中取得显著进展，但有效利用物品侧信息仍具挑战。真实物品由海量、异构且非结构化的元数据描述，其中决策相关信号往往隐含、嘈杂或深藏于长文本中。此外，特征重要性高度依赖上下文，不仅因物品而异，也随用户变化。现有方法多依赖标题、固定属性或静态摘要，难以实现个性化和细粒度理解。为此，我们提出CAIRO——一种面向大模型重排的用户上下文感知物品画像框架。CAIRO首先将原始元数据与评论结构化为客观特征与主观特质，并通过轻量级画像生成器，在服务时仅用少量开销为每对用户-物品选择最相关的信息。生成的画像简洁且上下文敏感，为大模型的排序决策提供有力的物品侧证据。实验表明，CAIRO持续提升基于大模型的重排效果，凸显了有效挖掘物品侧信息的重要性。
原文摘要 · Abstract (English)
While Large Language Models (LLMs) have significantly advanced reranking in recommendation, effectively leveraging item-side information remains challenging. Real-world items are described by vast, heterogeneous, and unstructured metadata, where decision-relevant signals are often implicit, noisy, or buried in long descriptions. Moreover, feature salience is highly context-dependent, varying not only across items but also across users. Existing methods often rely on item titles, fixed attributes, or static item summaries, which limit personalized and fine-grained item understanding. To bridge this gap, we propose CAIRO, a user context-aware item profiling framework for LLM-based reranking. CAIRO first structures raw metadata and reviews into objective features and subjective traits, and employs a lightweight profiler to select the most relevant information for each user-item pair with limited serving-time overhead. The resulting profiles are concise and context-specific, providing relevant item-side evidence for the LLM's ranking decision. Experiments show that CAIRO consistently improves LLM-based reranking, highlighting the importance of item profiling that effectively exploits vast item-side information.
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