Sensory-Aware Sequential Recommendation via Review-Distilled Representations
The research creates a framework to improve sequential recommendations by extracting structured sensory details from unstructured product reviews. It uses an LLM teacher to extract attributes like color and texture, which are then distilled into compact embeddings for use in sequence models. This allows recommendation systems to better understand item characteristics based on user feedback.