Distilling Transitional Pattern to Large Language Models for Multimodal Session-based Recommendation

Understand

Session-based recommendation (SBR) predicts the next item based on anonymous sessions.

• Traditional SBR explores user intents based on ID collaborations or auxiliary content.
• To further alleviate data sparsity and cold-start issues, recent Multimodal SBR (MSBR) methods utilize simplistic pre-trained models for modality learning but have limitations in semantic richness.
• Considering semantic reasoning abilities of Large Language Models (LLM), we focus on the LLM-enhanced MSBR scenario in this paper, which leverages LLM cognition for comprehensive multimodal representation generation, to enhance downstream MSBR.