用用户评论生成短片段，实现跨领域高效对话推荐
Snippet-based Conversational Recommender System
- 用大模型将评论转为精炼片段，捕捉多样用户表达
- 在3000-10000个候选物品中达Hits@10 0.25-0.55
- 无需标注数据，适配餐厅/书籍/服装等多领域
对话式推荐系统通过交互对话获取用户偏好并提供个性化推荐。现有方法常依赖预定义属性或昂贵的领域特定标注数据，限制了对多样化偏好和跨领域适应性的处理能力。本文提出SnipRec，一种资源高效的新型方法，利用用户生成内容（如客户评论）来捕捉更广泛的用户表达。通过大语言模型将评论与用户回复映射为简洁片段，SnipRec无需大量人工标注或微调即可表征用户偏好并检索相关项目。在餐厅、书籍和服装三个领域的实验表明，基于片段的表示优于文档和句子级表示，在3000至10000个候选物品下实现Hits@10为0.25-0.55，并能有效处理自由格式用户回复。
原文摘要 · Abstract (English)
Conversational Recommender Systems (CRS) engage users in interactive dialogues to gather preferences and provide personalized recommendations. While existing studies have advanced conversational strategies, they often rely on predefined attributes or expensive, domain-specific annotated datasets, which limits their flexibility in handling diverse user preferences and adaptability across domains. We propose SnipRec, a novel resource-efficient approach that leverages user-generated content, such as customer reviews, to capture a broader range of user expressions. By employing large language models to map reviews and user responses into concise snippets, SnipRec represents user preferences and retrieves relevant items without the need for intensive manual data collection or fine-tuning. Experiments across the restaurant, book, and clothing domains show that snippet-based representations outperform document- and sentence-based representations, achieving Hits@10 of 0.25-0.55 with 3,000 to 10,000 candidate items while successfully handling free-form user responses.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。