用大模型提示词实现跨域推荐，效果超越现有方法。
Cross-Domain Recommendation Meets Large Language Models
- 设计两种针对跨域推荐的提示词，利用大模型推理能力
- 在多个领域组合上，评分预测与排序任务均优于基线
- 适合数据少或追求简洁系统的场景，无需复杂架构
跨域推荐（CDR）是解决单域推荐系统冷启动问题的有前景方案。然而，现有CDR模型依赖复杂的神经网络结构、大规模数据集和大量计算资源，在数据稀缺或强调简洁性的场景中表现不佳。本文利用大语言模型（LLMs）的推理能力，探索其在多种领域对上的跨域推荐性能。我们提出两种专为CDR设计的新提示词，实验证明，当提示设计得当时，大模型在评分预测和排序任务中，于多个领域组合下均超越当前最优的CDR基线。该工作弥合了大模型与推荐系统之间的鸿沟，展示了大模型作为有效跨域推荐器的潜力。
原文摘要 · Abstract (English)
Cross-domain recommendation (CDR) has emerged as a promising solution to the cold-start problem, faced by single-domain recommender systems. However, existing CDR models rely on complex neural architectures, large datasets, and significant computational resources, making them less effective in data-scarce scenarios or when simplicity is crucial. In this work, we leverage the reasoning capabilities of large language models (LLMs) and explore their performance in the CDR domain across multiple domain pairs. We introduce two novel prompt designs tailored for CDR and demonstrate that LLMs, when prompted effectively, outperform state-of-the-art CDR baselines across various metrics and domain combinations in the rating prediction and ranking tasks. This work bridges the gap between LLMs and recommendation systems, showcasing their potential as effective cross-domain recommenders.
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