Architecture Design of Recommendation System Based on Large Multimodal Agents

Exploiting the large language models, autonomous agents have been widely developed for tackling decision-making and complex tasks in interactive environments. With the development of LLMs, multimodal agents have emerged with the ability of understanding not only text, but also images, sounds and videos. Therefore, multimodal agents are naturally applicable to generating recommendation information. However, few solutions focus on solving the intelligent recommendation problems on mobile phones, particularly in actively collecting and analyzing users preferences. In this paper, we propose an architecture for autonomous recommendation agents on cellphones, which can actively collect and understand multimodal data without restrictions on application layouts and graphical interface. We also introduce detail methods and workflows of the proposed agent. To this end, we capture a complete view of the proposed system.