Personalized Recommendation Systems using Multimodal, Autonomous, Multi Agent Systems

TL;DR

This paper presents a sophisticated multimodal, multi-agent recommendation system leveraging advanced AI models like Gemini-1.5-pro and LLaMA-70B to enhance personalized e-commerce customer experiences through adaptive, real-time, multimodal interactions.

Contribution

It introduces a novel multi-agent, multimodal architecture integrating cutting-edge AI models for improved personalized recommendations in e-commerce.

Findings

Effective multimodal integration improves recommendation accuracy.

Real-time data fetch enhances user experience.

Adaptive learning tailors recommendations to user preferences.

Abstract

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.