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arXiv · 2610.27898

SPAR: Stepwise Personalized Autoregressive Recommendation with Prefix-Balanced Semantic IDs

Chunrui Zhang·Gaoyun Lin·Chang Zhou·Zheng Hu·Qiran Song·Shimin Cai·Tao Zhou
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Abstract

Semantic ID (SID)-based generative recommendation retrieves items through successive token-level decisions, making personalization at each decoding step essential. However, existing methods insufficiently account for prefix-dependent user evidence and imbalanced historical interaction loads among candidate branches. This can cause irreversible branch-selection errors and favor popular paths. We propose SPAR, a generative recommendation framework that jointly improves stepwise personalization and SID construction. Its PATH Decoder retrieves prefix-relevant evidence from compressed long-term user memories and combines it with recent behavior, user profiles, and event context to guide each token decision. Its RQ-PIB module balances historical interaction loads among sibling branches under each parent prefix while preserving semantic structure. Experiments on large-scale Youku production data show an average relative HitRate improvement of 2.66% over the strongest baseline. SPAR also increases target-prefix retention by 5.79 percentage points and reduces the traffic-weighted largest child-branch share by 32.84% across SID levels. In a 7-day online A/B test, SPAR serves 10% of Youku's production traffic and improves average watch time per user by 3.95% and play conversion rate by 3.38%. These results demonstrate more reliable hierarchical decoding, balanced SID branches, and improved user engagement in industrial recommendation.

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Chunrui Zhang, Gaoyun Lin, Chang Zhou, Zheng Hu, Qiran Song, Shimin Cai, Tao Zhou. 2026-10-20. SPAR: Stepwise Personalized Autoregressive Recommendation with Prefix-Balanced Semantic IDs. https://arxiv.org/abs/2610.27898

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