基于多模态大模型的智能商品推荐系统：技术革新与应用实践

Intelligent Product Recommendation System Based on Multimodal Large Model: Technological Innovation and Application Practice

摘要: 当前电子商务推荐系统面临用户意图理解不足、多模态信息融合困难、冷启动问题严重等核心挑战，传统算法在复杂购物场景下的局限性日益凸显。本研究提出基于多模态大模型的智能商品推荐系统，通过构建“视觉–文本–行为”三位一体的多模态融合架构，利用大模型强大的语义理解和推理能力，实现用户需求的深度洞察和精准匹配。研究采用PreTrain-PostTrain-Application三阶段迭代范式，结合Agentic Retrieval-Augmented Generation (ARAG)框架，将推荐系统转化为语义推理与多智能体协作问题。实验结果表明，该系统在综合电商平台场景下实现了转化率提升28%~37%、点击率提升42%、用户停留时长增加18%的显著效果。技术架构采用轻量化设计，推理延迟控制在500 ms以内，满足大规模电商场景的实时性要求。本研究为电商推荐系统的智能化转型提供了完整的技术方案和实施路径，对推动行业技术进步具有重要意义。

Abstract: Current e-commerce recommendation systems face core challenges such as insufficient understanding of user intent, difficulties in multimodal information fusion, and severe cold-start problems. The limitations of traditional algorithms in complex shopping scenarios are becoming increasingly apparent. This research proposes an intelligent product recommendation system based on a multimodal large model. By constructing a three-in-one multimodal fusion architecture of “visual-text-behavior”, it leverages the powerful semantic understanding and reasoning capabilities of the large model to achieve deep insight and accurate matching of user needs. The research adopts a three-stage iterative paradigm of PreTrain-PostTrain-Application, combined with the Agentic Retrieval-Augmented Generation (ARAG) framework, transforming the recommendation system into a semantic reasoning and multi-agent collaboration problem. Experimental results show that the system achieves significant improvements in conversion rate (28%~37%), click-through rate (42%), and user dwell time (18%) in comprehensive e-commerce platform scenarios. The technical architecture adopts a lightweight design, with inference latency controlled within 500 ms, meeting the real-time requirements of large-scale e-commerce scenarios. This research provides a complete technical solution and implementation path for the intelligent transformation of e-commerce recommendation systems, which is of great significance for promoting technological progress in the industry.