系统分析LLM做推荐特征提取的关键因素，发现组合最佳设计可显著提升效果。
What Matters in LLM-Based Feature Extractor for Recommender? A Systematic Analysis of Prompts, Models, and Adaptation
- 将LLM特征提取拆解为四个模块，独立测试各环节设计
- 在4个数据集上实现NDCG@5提升18.7%，HR@5提升12.7%
- 适合想系统优化推荐模型的开发者和研究者
利用大语言模型生成语义特征已成为增强序列推荐系统的重要范式。该过程通常包含三个阶段：处理物品文本、使用LLM提取特征、对下游模型进行特征适配。然而现有方法在提示设计、架构和适配策略上差异较大，难以公平比较设计选择并识别真正驱动性能的因素。本文提出RecXplore，一个模块化分析框架，将LLM作为特征提取器的流程分解为四个模块：数据处理、语义特征提取、特征适配和序列建模。不提出新方法，而是重新梳理已有技术，实现对每个模块的独立系统探索。在四个公开数据集上的实验表明，仅通过组合现有技术中的最优设计，无需全搜索即可在NDCG@5上比强基线提升18.7%，在HR@5上提升12.7%。结果凸显了模块化基准测试在识别有效设计模式和推动标准化研究方面的价值。
原文摘要 · Abstract (English)
Using Large Language Models (LLMs) to generate semantic features has been demonstrated as a powerful paradigm for enhancing Sequential Recommender Systems (SRS). This typically involves three stages: processing item text, extracting features with LLMs, and adapting them for downstream models. However, existing methods vary widely in prompting, architecture, and adaptation strategies, making it difficult to fairly compare design choices and identify what truly drives performance. In this work, we propose RecXplore, a modular analytical framework that decomposes the LLM-as-feature-extractor pipeline into four modules: data processing, semantic feature extraction, feature adaptation, and sequential modeling. Instead of proposing new techniques, RecXplore revisits and organizes established methods, enabling systematic exploration of each module in isolation. Experiments on four public datasets show that simply combining the best designs from existing techniques without exhaustive search yields up to 18.7% relative improvement in NDCG@5 and 12.7% in HR@5 over strong baselines. These results underscore the utility of modular benchmarking for identifying effective design patterns and promoting standardized research in LLM-enhanced recommendation.
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