TAAL: Mitigating Early Beam Pruning in Generative Recommendation via Temporal Autoregressive Alignment

2026-08-29

AI Summary

该研究通过提出Temporal Autoregressive Alignment方法解决了生成式推荐中由于早期束搜索导致的真实SID被过早剪枝的问题，提高了推荐准确性和SID生存率。

Abstract

Generative recommendation encodes items as hierarchical semantic identifiers (SIDs) and retrieves the next item through autoregressive decoding. Standard next-token prediction, however, does not explicitly cover the multimodal transitions present in interaction sequences, leaving the ground-truth SID vulnerable to irreversible pruning at early beam-search branches. Across three public benchmarks, we find that 91.9\%--96.6\% of retrieval failures occur within the first two decoding steps. We therefore propose Temporal Autoregressive Alignment (TAAL). During training, TAAL constructs a joint $(c_1,c_2)$ soft target from historical transitions and aligns the early-prefix distribution with a forward KL objective. During inference, it calibrates candidate scores with pointwise mutual information (PMI) to reduce the influence of globally frequent prefixes. On Amazon Beauty, Instruments, and Yelp, TAAL improves NDCG@10 over the standard baseline by 39.5\%, 6.7\%, and 28.6\%, respectively, while increasing full-SID survival by 3.9\%--16.6\%. Beam-width analysis further shows that the relative survival gain grows as the beam narrows, reaching 39.4\% at $B=5$.

Problem

• Generative Recommendation
• Beam Pruning
• Multimodal Transitions
• Autoregressive Decoding
• Retrieval Failures

Innovation

• Temporal Autoregressive Alignment
• early beam pruning
• pointwise mutual information
• autoregressive decoding
• forward KL objective