Decision-Native Recommendation with Large Language Models

Overview & Synopsis

Large language models (LLMs) bring world knowledge to user preference modeling in sequential recommendation, but generation-based recommenders express next-item predictions indirectly as text. Generated outputs may fail to identify a unique candidate item, depend on candidate presentation order in the prompt, and lack explicit confidence in the recommendation. Recent studies explore Jev, a model for fast option selection with explicit confidence. Inspired by Jev, we introduce decision-native recommendation, a new paradigm that jointly models item choice and recommendation confidence using LLM representations. It requires valid candidate selection, consistency under candidate reordering, and confidence that reflects recommendation accuracy. We propose Direct Item Choice Estimation (DICE), which learns a personalized next-item distribution over supplied candidates from user interaction history and recommends the highest-scoring item with calibrated confidence. Tree-structured attention shares history computation across isolated candidate branches for order-consistent candidate scoring in a single forward pass. On three recommendation datasets, DICE achieves higher top-one accuracy than all evaluated baselines. Separate evaluations show unchanged item choices under tested candidate reorderings and end-to-end speedups of up to approximately $6\times$ over the evaluated generation-based methods. Its confidence estimates support selective recommendation by identifying more reliable predictions and guide additional computation for low-confidence examples in offline routing.