TDE: Target-conditioned Decision Evidence for Generative Recommendation

Generative recommendation (GR) autoregressively generates semantic token sequences for the next item, but its generation objective specifies what target to generate without explicitly supervising which historical behaviors support that target. We introduce Target-conditioned Decision Evidence (TDE), a new supervision framework that converts target-conditioned decision explanations into representation learning signals. Given an observed training target, TDE uses a reference model to identify which historical behaviors contribute to generating that specific target. We adapt Transformer attribution to autoregressive encoder-decoder recommendation by defining target scores over generated target tokens and tracing target-to-history contributions through decoder-to-encoder cross-attention. The resulting positive decision evidence is normalized into a soft distribution over historical behaviors, from which two independently perturbed evidence-grounded views supervise a trainable recommender through a contrastive objective. At inference time, TDE introduces no target input: the trained recommender performs standard autoregressive generation from the history alone. Experiments show that TDE consistently improves GRs. Controlled comparisons further show that its gains cannot be explained by heuristic augmentation, generic contrastive learning, target-supervised sequence pairing, or uniform token weighting. Additional analyses reveal that more target-relevant attribution provides more effective supervision, that evidence-guided augmentation and dual-view grounding play complementary roles, and that evidence transfer depends on alignment between the attribution pathway and the backbone’s decision objective. TDE establishes an explanation-to-supervision paradigm for GRs: learning not merely from user history, but from the historical evidence that supports a specific target decision.