Leveraging multimodal large language model for multimodal sequential recommendation.

ABSTRACT: Multimodal large language models (MLLMs) have demonstrated remarkable superiority in various vision-language tasks due to their unparalleled cross-modal comprehension capabilities and extensive world knowledge, offering promising research paradigms to address the insufficient information exploitation in conventional multimodal recommendation systems. Despite significant advances in existing recommendation approaches based on large language models, they still exhibit notable limitations in multimodal feature recognition and dynamic preference modeling, particularly in handling sequential data effectively and most of them predominantly rely on unimodal user-item interaction information, failing to adequately explore the cross-modal preference differences and the dynamic evolution of user int

SUBMITTER: Wang Z 

PROVIDER: S-EPMC12332125 | biostudies-literature | 2025 Aug

REPOSITORIES: biostudies-literature