Training with Missed Targets in Generative Recommendation: Separating Supervision from Probability Competition
The study investigated adding supervision by training models on items that were missed during recommendation, which changes how retrieved targets are weighted and introduces competition with returned items. The authors compare three loss functions to isolate these effects, concluding that group competition, not the extra supervision itself, is often the main source of degradation. They recommend testing candidate completion for each generator individually.