DiscRec: Disentangled Semantic-Collaborative Modeling for Generative Recommendation

• URL: http://arxiv.org/abs/2506.15576v2
• Date: Sun, 22 Jun 2025 14:01:07 GMT
• Title: DiscRec: Disentangled Semantic-Collaborative Modeling for Generative Recommendation
• Authors: Chang Liu, Yimeng Bai, Xiaoyan Zhao, Yang Zhang, Fuli Feng, Wenge Rong,
• Abstract summary: Generative recommendation is emerging as a powerful paradigm that directly generates item predictions.<n>Current methods face two key challenges: token-item misalignment and semantic-collaborative signal entanglement.<n>We propose DiscRec, a novel framework that enables Disentangled Semantic-Collaborative signal modeling.
• Score: 33.152693125551785
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

Related papers

• LLM2Rec: Large Language Models Are Powerful Embedding Models for Sequential Recommendation [49.78419076215196]
  Sequential recommendation aims to predict users' future interactions by modeling collaborative filtering (CF) signals from historical behaviors of similar users or items.<n>Traditional sequential recommenders rely on ID-based embeddings, which capture CF signals through high-order co-occurrence patterns.<n>Recent advances in large language models (LLMs) have motivated text-based recommendation approaches that derive item representations from textual descriptions.<n>We argue that an ideal embedding model should seamlessly integrate CF signals with rich semantic representations to improve both in-domain and out-of-domain recommendation performance.
  arXiv  Detail & Related papers  (2025-06-16T13:27:06Z)
• BBQRec: Behavior-Bind Quantization for Multi-Modal Sequential Recommendation [15.818669767036592]
  We propose a Behavior-Bind multi-modal Quantization for Sequential Recommendation (BBQRec) featuring dual-aligned quantization and semantics-aware sequence modeling.<n>BBQRec disentangles modality-agnostic behavioral patterns from noisy modality-specific features through contrastive codebook learning.<n>We design a discretized similarity reweighting mechanism that dynamically adjusts self-attention scores using quantized semantic relationships.
  arXiv  Detail & Related papers  (2025-04-09T07:19:48Z)
• Sparse Meets Dense: Unified Generative Recommendations with Cascaded Sparse-Dense Representations [22.48125906976824]
  We introduce the Cascaded Organized Bi-Represented generAtive retrieval framework, which integrates sparse semantic IDs and dense vectors through a cascading process.<n>Our method alternates between generating these representations by first generating sparse IDs, which serve as conditions to aid in the generation of dense vectors.<n>During inference, COBRA employs a coarse-to-fine strategy, starting with sparse ID generation and refining them into dense vectors via the generative model.
  arXiv  Detail & Related papers  (2025-03-04T10:00:05Z)
• EAGER: Two-Stream Generative Recommender with Behavior-Semantic Collaboration [63.112790050749695]
  We introduce EAGER, a novel generative recommendation framework that seamlessly integrates both behavioral and semantic information. We validate the effectiveness of EAGER on four public benchmarks, demonstrating its superior performance compared to existing methods.
  arXiv  Detail & Related papers  (2024-06-20T06:21:56Z)
• Diffusion-based Contrastive Learning for Sequential Recommendation [6.3482831836623355]
  We propose a Context-aware Diffusion-based Contrastive Learning for Sequential Recommendation, named CaDiRec.<n>CaDiRec employs a context-aware diffusion model to generate alternative items for the given positions within a sequence.<n>We train the entire framework in an end-to-end manner, with shared item embeddings between the diffusion model and the recommendation model.
  arXiv  Detail & Related papers  (2024-05-15T14:20:37Z)
• Local Consensus Enhanced Siamese Network with Reciprocal Loss for Two-view Correspondence Learning [35.5851523517487]
  Two-view correspondence learning usually establish an end-to-end network to jointly predict correspondence reliability and relative pose. We propose a Local Feature Consensus (LFC) plugin block to augment the features of existing models. We extend existing models to a Siamese network with a reciprocal loss that exploits the supervision of mutual projection.
  arXiv  Detail & Related papers  (2023-08-06T22:20:09Z)
• Object Segmentation by Mining Cross-Modal Semantics [68.88086621181628]
  We propose a novel approach by mining the Cross-Modal Semantics to guide the fusion and decoding of multimodal features. Specifically, we propose a novel network, termed XMSNet, consisting of (1) all-round attentive fusion (AF), (2) coarse-to-fine decoder (CFD), and (3) cross-layer self-supervision.
  arXiv  Detail & Related papers  (2023-05-17T14:30:11Z)
• Contrastive Self-supervised Sequential Recommendation with Robust Augmentation [101.25762166231904]
  Sequential Recommendationdescribes a set of techniques to model dynamic user behavior in order to predict future interactions in sequential user data. Old and new issues remain, including data-sparsity and noisy data. We propose Contrastive Self-Supervised Learning for sequential Recommendation (CoSeRec)
  arXiv  Detail & Related papers  (2021-08-14T07:15:25Z)
• Online Multiple Object Tracking with Cross-Task Synergy [120.70085565030628]
  We propose a novel unified model with synergy between position prediction and embedding association. The two tasks are linked by temporal-aware target attention and distractor attention, as well as identity-aware memory aggregation model.
  arXiv  Detail & Related papers  (2021-04-01T10:19:40Z)
• Dual-Refinement: Joint Label and Feature Refinement for Unsupervised Domain Adaptive Person Re-Identification [51.98150752331922]
  Unsupervised domain adaptive (UDA) person re-identification (re-ID) is a challenging task due to the missing of labels for the target domain data. We propose a novel approach, called Dual-Refinement, that jointly refines pseudo labels at the off-line clustering phase and features at the on-line training phase. Our method outperforms the state-of-the-art methods by a large margin.
  arXiv  Detail & Related papers  (2020-12-26T07:35:35Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.