Training with Missed Targets in Generative Recommendation: Separating Supervision from Probability Competition
Generative recommenders return a limited candidate set and may omit observed targets before reranking. A training strategy appends these missed targets to reranker training lists, although inference still ranks only original candidates. This operation simultaneously changes retrieved-target weight, adds supervision over appended targets, and makes the two groups compete for probability. An append/no-append comparison therefore cannot explain changes in returned-item rankings. We construct three matched losses that hold retrieved-target weight fixed while introducing appended-target supervision and group competition separately. The intermediate loss trains within both groups but normalizes...
| Authors | Xuesi Wang, Yangbin Shi, Xiaolin Zheng |
|---|---|
| Primary category | cs.IR |
| Published | 7 October 2026 |
| Last updated at source | 7 October 2026 |
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