Title: Agents as Knowledge Integrator and Utilizer in Multimodal Recommendation
Primary area: other topics in machine learning (i.e., none of the above)
Keywords: Recommendation Agent, Multimodal Recommendation

Abstract: The proliferation of online multimodal content has driven the adoption of multimodal data in recommendation systems. Current studies either enhance item features with multimodal data or construct additional homogenous graphs via multimodal data. However, a significant semantic gap exists between multimodal data and recommendation tasks. This gap introduces modality-specific noise irrelevant to recommendation tasks when enhancing item features and results in homogenous graphs built on multimodal data that fail to adequately consider users' historical behaviors. Fortunately, the multimodal information understanding and contextual processing capabilities of large language models (LLMs) have emerged as a promising approach to bridging this semantic gap.

To this end, we propose AgentMMRec, a novel agent-based framework that bridges the semantic gap via two cooperative agents: an Integrator Agent that uses LLMs to infer user preferences and item properties from multimodal data and users' historical behaviors, storing knowledge in a knowledge memory; and a Utilizer Agent that refines traditional homogenous item-item graphs using there knowledge, constructs behavior- and multimodal-aware homogenous graphs, and performs knowledge-enhanced reranking in recommendation stage. Integrator Agent updates the memory based on feedback from reranking performance. Extensive experiments on real-world datasets demonstrate that AgentMMRec outperforms existing multimodal recommendation models and exhibits superior performance across various data sparsity scenarios. Additionally, AgentMMRec can enhance the performance of existing multimodal recommendation models by leveraging the constructed knowledge memory.