用大模型跨域推荐，能显著提升冷门领域推荐效果。
Uncovering Cross-Domain Recommendation Ability of Large Language Models
- 用用户购买历史和共用特征构建上下文提示，实现知识迁移。
- 在音乐与影视推荐中，准确率提升64.28%（MAP）。
- 适合研究大模型在推荐系统中的跨域应用者参考。
跨域推荐（CDR）旨在通过高资源领域知识迁移来增强低资源领域的物品检索能力。尽管大语言模型（LLM）在推荐系统中展现出潜力，但其在跨域知识迁移方面的有效性仍待探索。为此，我们提出LLM4CDR，一种新型的CDR流程：利用源域用户的购买历史序列及源-目标域间的共享特征，构建上下文感知的提示。大量实验表明，当使用参数量大的LLM且源-目标域间域差距较小时，LLM4CDR表现优异。例如，在电影与电视推荐中引入唱片与黑胶购买历史，可使MAP提升64.28%。我们进一步分析了源域数据、域差距、提示设计和LLM规模等关键因素对性能的影响。结果表明，采用单一紧密相关源域并搭配大型LLM时，效果最佳。这些发现为基于大模型的跨域推荐研究提供了重要方向。
原文摘要 · Abstract (English)
Cross-Domain Recommendation (CDR) seeks to enhance item retrieval in low-resource domains by transferring knowledge from high-resource domains. While recent advancements in Large Language Models (LLMs) have demonstrated their potential in Recommender Systems (RS), their ability to effectively transfer domain knowledge for improved recommendations remains underexplored. To bridge this gap, we propose LLM4CDR, a novel CDR pipeline that constructs context-aware prompts by leveraging users' purchase history sequences from a source domain along with shared features between source and target domains. Through extensive experiments, we show that LLM4CDR achieves strong performance, particularly when using LLMs with large parameter sizes and when the source and target domains exhibit smaller domain gaps. For instance, incorporating CD and Vinyl purchase history for recommendations in Movies and TV yields a 64.28 percent MAP 1 improvement. We further investigate key factors including source domain data, domain gap, prompt design, and LLM size, which impact LLM4CDR's effectiveness in CDR tasks. Our results highlight that LLM4CDR excels when leveraging a single, closely related source domain and benefits significantly from larger LLMs. These insights pave the way for future research on LLM-driven cross-domain recommendations.
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