标题： 基于大语言模型相关性判断的高斯过程回归的多模态项目评分方法 显示英文标题

标题： Multimodal Item Scoring for Natural Language Recommendation via Gaussian Process Regression with LLM Relevance Judgments

摘要： 自然语言推荐（NLRec）根据用户发出的自然语言请求与自然语言物品描述段落之间的相关性生成物品建议。 现有的NLRec方法通常使用密集检索（DR）通过用户请求嵌入和相关段落嵌入之间的内积聚合来计算物品相关性得分。 然而，DR将请求视为唯一的相关性标签，从而导致以查询嵌入为中心的单模态评分函数，这通常是查询相关性的弱代理。 为了更好地捕捉可能从复杂NLRec数据中出现的相关性评分函数的潜在多模态分布，我们提出了GPR-LLM，该方法使用高斯过程回归（GPR）结合大型语言模型（LLM）的相关性判断对候选段落子集进行处理。 在四个NLRec数据集和两个LLM骨干模型上的实验表明，具有径向基函数（RBF）核的GPR-LLM能够建模多模态相关性评分函数，其表现始终优于简单的单模态核（点积、余弦相似性），以及基线方法包括DR、交叉编码器和基于点的LLM相关性评分，提升幅度高达65%。 总体而言，GPR-LLM在最小的LLM标注预算内为NLRec提供了一种高效且有效的方法。 显示英文摘要

摘要： Natural Language Recommendation (NLRec) generates item suggestions based on the relevance between user-issued NL requests and NL item description passages. Existing NLRec approaches often use Dense Retrieval (DR) to compute item relevance scores from aggregation of inner products between user request embeddings and relevant passage embeddings. However, DR views the request as the sole relevance label, thus leading to a unimodal scoring function centered on the query embedding that is often a weak proxy for query relevance. To better capture the potential multimodal distribution of the relevance scoring function that may arise from complex NLRec data, we propose GPR-LLM that uses Gaussian Process Regression (GPR) with LLM relevance judgments for a subset of candidate passages. Experiments on four NLRec datasets and two LLM backbones demonstrate that GPR-LLM with an RBF kernel, capable of modeling multimodal relevance scoring functions, consistently outperforms simpler unimodal kernels (dot product, cosine similarity), as well as baseline methods including DR, cross-encoder, and pointwise LLM-based relevance scoring by up to 65%. Overall, GPR-LLM provides an efficient and effective approach to NLRec within a minimal LLM labeling budget.