Author

Yi-Ming Xu

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Book Open access Aug 2026

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

ReSOT is proposed, a unified framework that Re-balances Semantic ID learning via Optimal Transport for GR and provides a principled tokenization scheme that preserves relational structure while assigning codes in a collision-aware and semantics-consistent manner.

Renwu Geng, Yi-Ming Xu, Fengxin Li et al. · Proceedings of the 32nd ACM... · 0 citations

Book Open access Aug 2026

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

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...

Renwu Geng, Yiming Xu, Fengxin Li et al. · Proceedings of the 32nd ACM... · 0 citations