Personalized Recommendation Systems using Multimodal, Autonomous, Multi Agent Systems
This addresses improving customer service experiences in e-commerce, but it appears incremental as it builds on existing multi-agent and multimodal approaches.
The paper tackles personalized recommendation in e-commerce by developing a system using multimodal, autonomous multi-agent systems with LLMs like Gemini-1.5-pro and LLaMA-70B, resulting in optimized product recommendations and customer interactions through real-time data and adaptive learning.
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.