Beyond Negative Transfer: Disentangled Preference-Guided Diffusion for Cross-Domain Sequential Recommendation

• URL: http://arxiv.org/abs/2509.00389v1
• Date: Sat, 30 Aug 2025 06:56:56 GMT
• Title: Beyond Negative Transfer: Disentangled Preference-Guided Diffusion for Cross-Domain Sequential Recommendation
• Abstract summary: DPG-Diff is a novel Disentangled Preference-Guided Diffusion Model.<n>It decomposes user preferences into domain-invariant and domain-specific components, which jointly guide the reverse diffusion process.<n>It consistently outperforms state-of-the-art baselines across multiple metrics.
• Score: 13.331414627413674
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Cross-Domain Sequential Recommendation (CDSR) leverages user behaviors across domains to enhance recommendation quality. However, naive aggregation of sequential signals can introduce conflicting domain-specific preferences, leading to negative transfer. While Sequential Recommendation (SR) already suffers from noisy behaviors such as misclicks and impulsive actions, CDSR further amplifies this issue due to domain heterogeneity arising from diverse item types and user intents. The core challenge is disentangling three intertwined signals: domain-invariant preferences, domain-specific preferences, and noise. Diffusion Models (DMs) offer a generative denoising framework well-suited for disentangling complex user preferences and enhancing robustness to noise. Their iterative refinement process enables gradual denoising, making them effective at capturing subtle preference signals. However, existing applications in recommendation face notable limitations: sequential DMs often conflate shared and domain-specific preferences, while cross-domain collaborative filtering DMs neglect temporal dynamics, limiting their ability to model evolving user preferences. To bridge these gaps, we propose \textbf{DPG-Diff}, a novel Disentangled Preference-Guided Diffusion Model, the first diffusion-based approach tailored for CDSR, to or best knowledge. DPG-Diff decomposes user preferences into domain-invariant and domain-specific components, which jointly guide the reverse diffusion process. This disentangled guidance enables robust cross-domain knowledge transfer, mitigates negative transfer, and filters sequential noise. Extensive experiments on real-world datasets demonstrate that DPG-Diff consistently outperforms state-of-the-art baselines across multiple metrics.

Related papers

• Hierarchical Quantization with Domain-Adaptive Sparse Routing for Generative Cross-Domain Recommendation [52.29536950794571]
  HD-Rec is a unified generative framework for cross-domain recommendation.<n>It employs a hierarchical domain-aware quantizer that constructs semantic identifiers using globally shared coarse-level codebooks and adaptively routed fine-level codebooks.<n>Experiments show that HD-Rec consistently improves over competitive sequential, generative, and cross-domain recommendation baselines.
  arXiv  Detail & Related papers  (2026-08-07T09:15:19Z)
• From Clues to Generation: Language-Guided Conditional Diffusion for Cross-Domain Recommendation [8.759054108541394]
  Cross-domain Recommendation (CDR) exploits multi-domain correlations to alleviate data sparsity.<n>This paper proposes Language-Guided Conditional Diffusion for CDR (LGCD), a novel framework that integrates Large Language Models (LLMs) and diffusion models for inter-domain sequential recommendation.
  arXiv  Detail & Related papers  (2026-04-07T03:05:22Z)
• S2CDR: Smoothing-Sharpening Process Model for Cross-Domain Recommendation [27.908928867224777]
  Cross-domain recommendation (CDR) has emerged as a highly effective remedy for the user cold-start challenge.<n>We propose a novel paradigm of Smoothing-Sharpening Process Model for CDR to cold-start users, termed as S2CDR.<n>Our S2CDR significantly outperforms previous SOTA methods in a training-free manner.
  arXiv  Detail & Related papers  (2026-03-03T08:21:42Z)
• From Agnostic to Specific: Latent Preference Diffusion for Multi-Behavior Sequential Recommendation [28.437926520491445]
  Multi-behavior sequential recommendation (MBSR) aims to learn the dynamic and heterogeneous interactions of users' multi-behavior sequences.<n>Recent concerns are shifting from behavior-fixed to behavior-specific recommendation.<n>We propose textbfFatsMB, a framework based diffusion model that guides preference generation.
  arXiv  Detail & Related papers  (2026-02-26T15:48:09Z)
• LLM-EDT: Large Language Model Enhanced Cross-domain Sequential Recommendation with Dual-phase Training [53.539682966282534]
  Cross-domain Sequential Recommendation (CDSR) has been proposed to enrich user-item interactions by incorporating information from various domains.<n>Despite current progress, the imbalance issue and transition issue hinder further development of CDSR.<n>We propose an LLMs Enhanced Cross-domain Sequential Recommendation with Dual-phase Training (LLM-EDT)
  arXiv  Detail & Related papers  (2025-11-25T05:18:04Z)
• Align-for-Fusion: Harmonizing Triple Preferences via Dual-oriented Diffusion for Cross-domain Sequential Recommendation [5.661192070842017]
  Cross-domain sequential recommendation (CDSR) methods often follow an align-then-fusion paradigm.<n>We propose an align-for-fusion framework for CDSR to harmonize triple preferences via dual-oriented DMs, HorizonRec.<n>Experiments on four CDSR datasets from two distinct platforms demonstrate the effectiveness and robustness of HorizonRec.
  arXiv  Detail & Related papers  (2025-08-07T07:00:29Z)
• Enhancing Transferability and Consistency in Cross-Domain Recommendations via Supervised Disentanglement [13.553355329509243]
  Cross-domain recommendation aims to alleviate the data sparsity by transferring knowledge across domains.<n>Disentangled representation learning provides an effective solution to model complex user preferences.<n>We propose DGCDR, a GNN-enhanced encoder-decoder framework.
  arXiv  Detail & Related papers  (2025-07-23T01:29:45Z)
• LLM-RecG: A Semantic Bias-Aware Framework for Zero-Shot Sequential Recommendation [5.512301280728178]
  Zero-shot cross-domain sequential recommendation (ZCDSR) enables predictions in unseen domains without additional training or fine-tuning.<n>Recent advancements in large language models (LLMs) have significantly enhanced ZCDSR by facilitating cross-domain knowledge transfer.<n>We propose a novel semantic bias-aware framework that improves cross-domain alignment at both the item and sequential levels.
  arXiv  Detail & Related papers  (2025-01-31T15:43:21Z)
• Breaking Determinism: Fuzzy Modeling of Sequential Recommendation Using Discrete State Space Diffusion Model [66.91323540178739]
  Sequential recommendation (SR) aims to predict items that users may be interested in based on their historical behavior. We revisit SR from a novel information-theoretic perspective and find that sequential modeling methods fail to adequately capture randomness and unpredictability of user behavior. Inspired by fuzzy information processing theory, this paper introduces the fuzzy sets of interaction sequences to overcome the limitations and better capture the evolution of users' real interests.
  arXiv  Detail & Related papers  (2024-10-31T14:52:01Z)
• Dual Conditional Diffusion Models for Sequential Recommendation [63.82152785755723]
  We propose Dual Conditional Diffusion Models for Sequential Recommendation (DCRec)<n>DCRec integrates implicit and explicit information by embedding dual conditions into both the forward and reverse diffusion processes.<n>This allows the model to retain valuable sequential and contextual information while leveraging explicit user-item interactions to guide the recommendation process.
  arXiv  Detail & Related papers  (2024-10-29T11:51:06Z)
• Towards Unified and Adaptive Cross-Domain Collaborative Filtering via Graph Signal Processing [48.04521184170971]
  Cross-Domain Recommendation (CDR) has emerged as a promising solution by leveraging dense domains to improve recommendations in sparse target domains.<n>We propose CGSP, a unified and adaptive CDR framework based on graph signal processing (GSP)
  arXiv  Detail & Related papers  (2024-07-17T07:52:45Z)
• Towards Open-world Cross-Domain Sequential Recommendation: A Model-Agnostic Contrastive Denoising Approach [16.09514981871128]
  Cross-domain sequential recommendation (CDSR) aims to address the data sparsity problems that exist in traditional sequential recommendation (SR) systems. In real-world recommender systems, CDSR scenarios usually consist of a majority of long-tailed users with sparse behaviors and cold-start users who only exist in one domain.
  arXiv  Detail & Related papers  (2023-11-08T15:33:06Z)
• Diffusion Recommender Model [85.9640416600725]
  We propose a novel Diffusion Recommender Model (named DiffRec) to learn the generative process in a denoising manner.<n>To retain personalized information in user interactions, DiffRec reduces the added noises and avoids corrupting users' interactions into pure noises like in image synthesis.
  arXiv  Detail & Related papers  (2023-04-11T04:31:00Z)
• Cyclically Disentangled Feature Translation for Face Anti-spoofing [61.70377630461084]
  We propose a novel domain adaptation method called cyclically disentangled feature translation network (CDFTN) CDFTN generates pseudo-labeled samples that possess: 1) source domain-invariant liveness features and 2) target domain-specific content features, which are disentangled through domain adversarial training. A robust classifier is trained based on the synthetic pseudo-labeled images under the supervision of source domain labels.
  arXiv  Detail & Related papers  (2022-12-07T14:12:34Z)
• Diverse Preference Augmentation with Multiple Domains for Cold-start Recommendations [92.47380209981348]
  We propose a Diverse Preference Augmentation framework with multiple source domains based on meta-learning. We generate diverse ratings in a new domain of interest to handle overfitting on the case of sparse interactions. These ratings are introduced into the meta-training procedure to learn a preference meta-learner, which produces good generalization ability.
  arXiv  Detail & Related papers  (2022-04-01T10:10:50Z)

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.