DiscRec: Disentangled Semantic-Collaborative Modeling for Generative Recommendation

Generative recommendation is emerging as a powerful paradigm that directly generates item predictions, moving beyond traditional matching-based approaches. However, current methods face two key challenges: token-item misalignment, where uniform token-level modeling ignores item-level granularity that is critical for collaborative signal learning, and semantic-collaborative signal entanglement, where collaborative and semantic signals exhibit distinct distributions yet are fused in a unified embedding space, leading to conflicting optimization objectives that limit the recommendation performance.

@article{liu2025_2506.15576,
  title={ DiscRec: Disentangled Semantic-Collaborative Modeling for Generative Recommendation },
  author={ Chang Liu and Yimeng Bai and Xiaoyan Zhao and Yang Zhang and Fuli Feng and Wenge Rong },
  journal={arXiv preprint arXiv:2506.15576},
  year={ 2025 }
}