How can LLMs power personalized e-commerce recommendations?
Personalized Recommendation Systems using Multimodal, Autonomous, Multi Agent Systems
October 29, 2024
https://arxiv.org/pdf/2410.19855-
Personalized Product Recommendations: This paper proposes a multi-agent AI system for creating more personalized e-commerce recommendations by analyzing not just user data and history, but also incorporating real-time trends and user-provided images.
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- LLM-Powered Agents: The system utilizes powerful LLMs like Gemini and LLaMA-70B to power its agents, allowing for sophisticated natural language processing and real-time information retrieval.
- Multi-Agent Collaboration: Individual agents specialize in product recommendation, image analysis, and market trend analysis. They work in parallel and collaborate to generate comprehensive recommendations.
- Multimodal Data: The system leverages both text data (user queries, product descriptions) and visual data (user-provided images) for a richer understanding of user needs.
- Dynamic Information Retrieval: Recommendations are not solely based on static data. Agents dynamically fetch information from the web, ensuring recommendations are up-to-date with current trends and product availability.