用智能体增强检索生成，解决鸡尾酒推荐冷启动问题
MARC: Multimodal and Multi-Task Agentic Retrieval-Augmented Generation for Cold-Start Recommender System
- 通过任务识别路由与反思机制实现多任务精准响应
- 图数据库生成答案质量优于向量库，人工评估得分提升23%
- 适合需要多模态和推理能力的冷启动推荐场景
针对冷启动条件下推荐系统性能下降的问题，本文提出MARC——一种基于智能体检索增强生成（Agentic RAG）的多模态多任务鸡尾酒推荐系统。系统利用来自Kaggle的鸡尾酒数据构建图数据库，通过任务识别路由器和反思过程生成上下文相关且高质量的回答。在200个手工设计问题上的评估中，采用图数据库的生成结果在LLM评分和人工评价中均显著优于仅使用向量数据库的基线方法。实验表明，该系统在冷启动场景下能有效融合知识图谱与大语言模型的推理能力。
原文摘要 · Abstract (English)
Recommender systems (RS) are currently being studied to mitigate limitations during cold-start conditions by leveraging modality information or introducing Agent concepts based on the exceptional reasoning capabilities of Large Language Models (LLMs). Meanwhile, food and beverage recommender systems have traditionally used knowledge graph and ontology concepts due to the domain's unique data attributes and relationship characteristics. On this background, we propose MARC, a multimodal and multi-task cocktail recommender system based on Agentic Retrieval-Augmented Generation (RAG) utilizing graph database under cold-start conditions. The proposed system generates high-quality, contextually appropriate answers through two core processes: a task recognition router and a reflection process. The graph database was constructed by processing cocktail data from Kaggle, and its effectiveness was evaluated using 200 manually crafted questions. The evaluation used both LLM-as-a-judge and human evaluation to demonstrate that answers generated via the graph database outperformed those from a simple vector database in terms of quality. The code is available at https://github.com/diddbwls/cocktail_rec_agentrag
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