用大模型生成商品使用场景，让推荐更符合真实需求。
Generation and annotation of item usage scenarios in e-commerce using large language models
- 用大模型生成商品搭配的使用情境，而非仅依赖历史数据
- 人工评估显示85%生成场景合理，具备实际参考价值
- 适合做个性化推荐、电商智能搭配系统的研究者
互补推荐在电商中起关键作用，但其关系主观且因人而异，难以通过历史数据推断。与依赖统计共现的传统方法不同，本文关注驱动商品搭配的使用背景。我们假设用户通过想象具体使用场景来选择互补商品。基于此，探索利用大语言模型（LLMs）生成商品使用场景，作为构建互补推荐系统的基础。首先通过人工标注评估生成场景的合理性，结果表明约85%的生成场景被判定为合理，证明大模型能有效生成真实可信的使用情境。
原文摘要 · Abstract (English)
Complementary recommendations suggest combinations of useful items that play important roles in e-commerce. However, complementary relationships are often subjective and vary among individuals, making them difficult to infer from historical data. Unlike conventional history-based methods that rely on statistical co-occurrence, we focus on the underlying usage context that motivates item combinations. We hypothesized that people select complementary items by imagining specific usage scenarios and identifying the needs in such situations. Based on this idea, we explored the use of large language models (LLMs) to generate item usage scenarios as a starting point for constructing complementary recommendation systems. First, we evaluated the plausibility of LLM-generated scenarios through manual annotation. The results demonstrated that approximately 85% of the generated scenarios were determined to be plausible, suggesting that LLMs can effectively generate realistic item usage scenarios.
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