Personalized Recommendation Systems using Multimodal, Autonomous, Multi Agent Systems

• URL: http://arxiv.org/abs/2410.19855v1
• Date: Tue, 22 Oct 2024 14:11:26 GMT
• Title: Personalized Recommendation Systems using Multimodal, Autonomous, Multi Agent Systems
• Authors: Param Thakkar, Anushka Yadav,
• Abstract summary: 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.
• Score: 0.6629765271909505
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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