将大模型与协同过滤结合，提升对话推荐系统对新电影的推荐精度。
Collaborative Retrieval for Large Language Model-based Conversational Recommender Systems
- 用协同过滤检索增强大模型生成，融合行为数据与语义理解。
- 在两个电影对话数据集上，对新上映影片的推荐准确率显著提升。
- 适合关注对话推荐中冷启动问题的研究者和开发者。
对话推荐系统（CRS）通过与用户交互对话提供个性化推荐。尽管大语言模型（LLMs）能更好理解上下文中的用户偏好，但通常难以利用行为数据——而行为数据在经典协同过滤（CF）方法中已被证明至关重要。为此，我们提出 CRAG：基于大模型的协同检索增强生成方法。据我们所知，CRAG 是首个将前沿大模型与协同过滤结合用于对话推荐的方法。在两个公开电影对话推荐数据集（我们重新整理的 Reddit 数据集，命名为 Reddit-v2，以及 Redial）上的实验表明，相比多个 CRS 基线，CRAG 在物品覆盖率和推荐性能上均表现更优。尤其值得注意的是，性能提升主要体现在对近期上映电影的推荐准确性上。代码与数据已开源。
原文摘要 · Abstract (English)
Conversational recommender systems (CRS) aim to provide personalized recommendations via interactive dialogues with users. While large language models (LLMs) enhance CRS with their superior understanding of context-aware user preferences, they typically struggle to leverage behavioral data, which have proven to be important for classical collaborative filtering (CF)-based approaches. For this reason, we propose CRAG, Collaborative Retrieval Augmented Generation for LLM-based CRS. To the best of our knowledge, CRAG is the first approach that combines state-of-the-art LLMs with CF for conversational recommendations. Our experiments on two publicly available movie conversational recommendation datasets, i.e., a refined Reddit dataset (which we name Reddit-v2) as well as the Redial dataset, demonstrate the superior item coverage and recommendation performance of CRAG, compared to several CRS baselines. Moreover, we observe that the improvements are mainly due to better recommendation accuracy on recently released movies. The code and data are available at https://github.com/yaochenzhu/CRAG.
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