ReSOT: Re-balance Semantic ID with Optimal Transport for Generative Recommendation

Abstract

Generative recommendation (GR) reformulates sequential recommendation as an autoregressive generation problem, where items are represented as discrete semantic IDs. However, learning effective item tokenization is critical yet remains challenging. Most existing methods optimize tokenization in a point-wise or heuristic manner, which often leads to Relational Structure Distortion. We identify two main sources of this distortion: (1) point-wise reconstruction losses that are insufficient to preserve item--item relations, and (2) collision-mitigation--driven code assignment that disrupts semantic neighborhoods under many-to-one mappings. This issue is especially harmful for recommendation, since the task fundamentally depends on relative item similarity. To address this issue, we propose ReSOT, a unified framework that Re-balances Semantic ID learning via Optimal Transport for GR. ReSOT consists of two complementary components: a structure-aware reconstruction module that aligns item–item relations between the original and the reconstructed embedding space, and an unbalanced optimal transport-based soft quantization module that achieves a better trade-off between collision mitigation and semantic neighborhood preservation. Extensive experiments on real-world datasets show that ReSOT provides a principled tokenization scheme that preserves relational structure while assigning codes in a collision-aware and semantics-consistent manner. Our code is available at https://github.com/grw-zju/ReSOT.