# DiscRec: Disentangled Semantic-Collaborative Modeling for Generative Recommendation

arXiv: https://arxiv.org/abs/2506.15576 · HTML: https://mlsanity.com/paper/2506.15576/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Jun 2025.

## Built on by (most cited first, 8 of 8)

- [Latent Inter-User Difference Modeling for LLM Personalization](https://mlsanity.com/paper/2507.20849/) (2025)
- [Reinforced Strategy Optimization for Conversational Recommender Systems via Network-of-Experts](https://mlsanity.com/paper/2509.26093/) (2025)
- [UniGRec: Unified Generative Recommendation with Soft Identifiers for End-to-End Optimization](https://mlsanity.com/paper/2601.17438/) (2026)
- [PRISM: Purified Representation and Integrated Semantic Modeling for Generative Sequential Recommendation](https://mlsanity.com/paper/2601.16556/) (2026)
- [Trie-Aware Transformers for Generative Recommendation](https://mlsanity.com/paper/2602.21677/) (2026)
- [Intent-Driven Semantic ID Generation for Grounded Conversational News Recommendation](https://mlsanity.com/paper/2605.07613/) (2026)
- [EchoRec: Multi-Item Prediction-Empowered Generative Recommendation via Cycle-Consistent Preference Alignment](https://mlsanity.com/paper/2608.14011/) (2026)
- [SIDScope: A Diagnostic Resource for Semantic-ID Interfaces in Generative Recommendation](https://mlsanity.com/paper/2608.18779/) (2026)

## Abstract (arXiv)

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. To address these issues, we propose DiscRec, a novel framework that enables Disentangled Semantic-Collaborative signal modeling with flexible fusion for generative Recommendation. First, DiscRec introduces item-level position embeddings, assigned based on indices within each semantic ID, enabling explicit modeling of item structure in input token sequences. Second, DiscRec employs a dual-branch module to disentangle the two signals at the embedding layer: a semantic branch encodes semantic signals using original token embeddings, while a collaborative branch applies localized attention restricted to tokens within the same item to effectively capture collaborative signals. A gating mechanism subsequently fuses both branches while preserving the model's ability to model sequential dependencies. Extensive experiments on four real-world datasets demonstrate that DiscRec effectively decouples these signals and consistently outperforms state-of-the-art baselines. Our codes are available on https://github.com/Ten-Mao/DiscRec.