Research: Enhancing Generative Recommendation with LLM-Derived Language Tags
_reachsumit · x · 2026-07-31
Current LLM-based generative recommendation models typically cast recommendation as autoregressively generating an item's Semantic-ID (SID). However, a compact SID struggles to hold both content and collaborative signals simultaneously, impairing model inference.
This research proposes a novel framework that, without altering the backbone or retraining SIDs, uses LLM-derived personalized natural language audience tags as an auxiliary channel. This injects hierarchical collaborative cues during generation, effectively restoring collaborative signals in the recommendation process.
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