利用多式联运大型语言模型进行多式联运顺序推
Zhaoliang Wang1,2, Baisong Liu3, Weiming Huang1
1Faculty of Information Science and Engineering, Ningbo University, Ningbo, 315211, People's Republic of China.
Scientific reports
|August 7, 2025
概括
多式联网大型语言模型 (MLLMs) 通过融合多式联网功能和建模动态用户偏好来增强顺序推系统. MLLM-SRec通过利用MLLM来提高建议的准确性和稳定性,以获得更好的跨模式理解.
科学领域:
- 人工智能的人工智能
- 计算机科学 计算机科学
- 机器学习 机器学习
背景情况:
- 传统的多式联运推系统在信息利用不足,多式联运特征识别有限,动态偏好建模无效等方面扎.
- 现有的方法通常依赖于单模式数据,无法捕捉跨模式偏好和用户兴趣在连续交互中的演变.
- 多模式大语言模型 (MLLMs) 提供先进的跨模式理解和世界知识,为推系统增强提供了一个有前途的途径.
研究的目的:
- 引入 MLLM-SRec,一种利用 MLLM 来解决当前多式联运推系统的局限性的新型顺序推架构.
- 开发一种多式联络功能融合机制,用于统一的项目表示,使视觉和文本对齐,同时减轻跨式联络差异和噪音.
- 为动态用户偏好建模设计一个时间意识模块,并将其与思维链提示进行集成,以实现有效的知识传输.
主要方法:
- 开发了一种使用MLLM的多式特征融合机制,以创建统一的语义项目表示,确保视觉和文本数据之间的语义对齐.
- 实现了一个时间意识的用户行为理解模块,以捕捉用户偏好在顺序交互数据中的动态演变.
- 员工监督的微调与多步思维链相结合,促使优化从预先训练的MLLM到推任务的知识转移.
主要成果:
- 拟议的MLLM-SRec架构在四个基准数据集中实现了与最先进的基线相比的显著改进.
- 该方法大大提高了推结果的精度.
- 在多式联运顺序推场景中,MLLM-SRec表现出卓越的稳定性和适应性.
结论:
- 在顺序推中,MLLM-SRec有效地解决了多式联运特征识别和动态偏好建模的挑战.
- 该架构验证了MLLM在通过改进多式联络交互数据利用来推进顺序推任务方面的巨大潜力.
- 这些发现为多式联络序列推研究提供了新的方法见解,并强调了MLLM集成的好处.
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