COMPOTE: Collapse-Resistant Multiple-Hypothesis Learning For Sequential Recommendation

Next-item prediction is inherently ambiguous: a single interaction history often admits multiple plausible continuations across distinct catalogue regions. Stan- dard sequential recommenders compress these alternatives into a single prediction, conflating user intents. We introduce COllapse-resistant Multiple hyPOThesis lEarning (COMPOTE), a multi-hypothesis transformer trained with an annealed objective that transitions from uniform supervision to a winner-takes-all assign- ment. This mechanism forces hypotheses to specialize into distinct catalogue re- gions, while inference relies on an efficient, normalizer-free max-similarity rule. Theoretically, we show that in the population case, the optima solutionspartition the conditional next-item distribution according to a max-entropy quantization principle, while pooled scores preserve the Bayes-optimal ranking. We also prove a generalization bound scaling logarithmically with the number of hypotheses. Across three Amazon Reviews 2023 benchmarks, COMPOTE outperforms strong ID-based, generative, and multi-interest baselines using item IDs alone. Matched- capacity ablations confirm that performance gains stem directly from the annealed objective, preventing the head collapse observed in conventional multi-head archi- tectures, rather than added capacity, ensembling, or routing.