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arXiv · 2611.01136

SIDRec: End-to-End Semantic ID Representation and Modeling for Recommendation

Zhuohang Jiang·Yongsen Pan·Pangjing Wu·Zihuai Zhao·Zheng Hu·Yuxin Chen
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Abstract

Semantic IDs (SIDs) have emerged as a promising approach for discriminative recommendation by encoding continuous item rep- resentations into compact discrete identifiers. However, existing SID-based methods face two fundamental limitations: semantic mis- match between content semantics and collaborative preferences, and objective mismatch between SID construction and downstream recommendation optimization. To address these challenges, we propose SIDRec, an end-to-end recommendation-aware SID learn- ing framework that jointly models content and collaborative in- formation while aligning SID construction with recommendation objectives. Specifically, SIDRec introduces a self-updating fusion mechanism that explicitly integrates pretrained content represen- tations with collaborative embeddings initialized from a trained recommendation model, enabling preference-aware semantic mod- eling. Furthermore, SIDRec employs an MMoE-inspired joint opti- mization mechanism with shared experts and task-specific gates, allowing recommendation supervision to directly guide differen- tiable quantization and produce task-adaptive discrete representa- tions. Extensive experiments on three public benchmark datasets demonstrate that SIDRec consistently outperforms all competing methods across both AUC and GAUC, achieving up to 5.15% relative improvements over the strongest baselines. Further ablation stud- ies validate the effectiveness and complementarity of collaborative semantic modeling and end-to-end SID optimization.

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Zhuohang Jiang, Yongsen Pan, Pangjing Wu, Zihuai Zhao, Zheng Hu, Yuxin Chen. 2026-10-31. SIDRec: End-to-End Semantic ID Representation and Modeling for Recommendation. https://arxiv.org/abs/2611.01136

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