GenAIR

GenAIR: LLM-grounded archetype representations improve sequential recommendation

GenAIR is a framework that uses LLMs to infer 'archetype' profiles of items' ideal target audiences, generating richer item embeddings for sequential recommendation systems. A behavioral calibration objective aligns these semantic embeddings with actual user interaction patterns, closing the gap between language-space representations and real-world behavior. Experiments on three datasets show consistent improvements over state-of-the-art baselines across multiple sequential recommendation models.