# Personalized Recommendation Systems using Multimodal, Autonomous, Multi Agent Systems

arXiv: https://arxiv.org/abs/2410.19855 · HTML: https://mlsanity.com/paper/2410.19855/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 32 references that are in the map.
- First posted on arXiv 22 Oct 2024.

## Builds on (most cited first, 8 of 8)

- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [How Can Recommender Systems Benefit from Large Language Models: A Survey](https://mlsanity.com/paper/2306.05817/) (2023)
- [User Behavior Simulation with Large Language Model based Agents](https://mlsanity.com/paper/2306.02552/) (2023)
- [RecMind: Large Language Model Powered Agent For Recommendation](https://mlsanity.com/paper/2308.14296/) (2023)
- [Towards Topic-Guided Conversational Recommender System](https://mlsanity.com/paper/2010.04125/) (2020)
- [MACRec: a Multi-Agent Collaboration Framework for Recommendation](https://mlsanity.com/paper/2402.15235/) (2024)
- [Incorporating External Knowledge and Goal Guidance for LLM-based Conversational Recommender Systems](https://mlsanity.com/paper/2405.01868/) (2024)
- [MAS4POI: a Multi-Agents Collaboration System for Next POI Recommendation](https://mlsanity.com/paper/2409.13700/) (2024)

## Built on by (most cited first, 2 of 2)

- [A Survey on LLM-powered Agents for Recommender Systems](https://mlsanity.com/paper/2502.10050/) (2025)
- [AgenticTagger: Structured Item Representation for Recommendation with LLM Agents](https://mlsanity.com/paper/2602.05945/) (2026)

## Abstract (arXiv)

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.