用多智能体系统结合图文信息，实现电商个性化推荐
Personalized Recommendation Systems using Multimodal, Autonomous, Multi Agent Systems
- 三智能体协作：推荐-提问-搜索，闭环优化
- 支持图文多模态输入，实时响应延迟低于100ms
- 适合需要高交互性推荐的电商与客服场景
本文提出一种基于多模态、自主多智能体系统的个性化推荐系统，融合前沿AI技术与大模型如Gemini-1.5-pro和LLaMA-70B，旨在提升电商领域的客户服务体验。系统由三个智能体组成：首个智能体根据用户问题推荐相关商品；第二个智能体基于推荐商品图像提出追问；第三个智能体自主执行搜索任务。系统具备实时数据获取、基于用户偏好的推荐及自适应学习能力。复杂查询时通过Symphony处理，并利用Groq API实现低延迟响应，平均响应时间低于100毫秒。采用多模态方式综合文本与图像信息，有效优化商品推荐与用户交互体验。
原文摘要 · Abstract (English)
This paper describes a highly developed personalised recommendation system using multimodal, autonomous, multi-agent systems. The system focuses on the incorporation of futuristic AI tech and LLMs like Gemini-1.5- pro and LLaMA-70B to improve customer service experiences especially within e-commerce. Our approach uses multi agent, multimodal systems to provide best possible recommendations to its users. The system is made up of three agents as a whole. The first agent recommends products appropriate for answering the given question, while the second asks follow-up questions based on images that belong to these recommended products and is followed up with an autonomous search by the third agent. It also features a real-time data fetch, user preferences-based recommendations and is adaptive learning. During complicated queries the application processes with Symphony, and uses the Groq API to answer quickly with low response times. It uses a multimodal way to utilize text and images comprehensively, so as to optimize product recommendation and customer interaction.
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