Adaptive Graph Integration for Cross-Domain Recommendation via Heterogeneous Graph Coordinators

• URL: http://arxiv.org/abs/2410.11719v2
• Date: Thu, 10 Jul 2025 14:28:19 GMT
• Title: Adaptive Graph Integration for Cross-Domain Recommendation via Heterogeneous Graph Coordinators
• Authors: Hengyu Zhang, Chunxu Shen, Xiangguo Sun, Jie Tan, Yu Rong, Chengzhi Piao, Hong Cheng, Lingling Yi,
• Abstract summary: Leveraging multi-domain data can improve recommendation systems by enriching user insights and mitigating data sparsity in individual domains.<n>We propose HAGO, a novel framework with textbfHeterogeneous textbfAdaptive textbfGraph cotextbfOrdinators.<n>Our framework adaptively adjusts the connections between coordinators and multi-domain graph nodes to enhance beneficial inter-domain interactions.
• Score: 31.05975545409408
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In the digital era, users typically interact with diverse items across multiple domains (e.g., e-commerce, streaming platforms, and social networks), generating intricate heterogeneous interaction graphs. Leveraging multi-domain data can improve recommendation systems by enriching user insights and mitigating data sparsity in individual domains. However, integrating such multi-domain knowledge for cross-domain recommendation remains challenging due to inherent disparities in user behavior and item characteristics and the risk of negative transfer, where irrelevant or conflicting information from the source domains adversely impacts the target domain's performance. To tackle these challenges, we propose HAGO, a novel framework with \textbf{H}eterogeneous \textbf{A}daptive \textbf{G}raph co\textbf{O}rdinators, which dynamically integrates multi-domain graphs into a cohesive structure. HAGO adaptively adjusts the connections between coordinators and multi-domain graph nodes to enhance beneficial inter-domain interactions while alleviating negative transfer. Furthermore, we introduce a universal multi-domain graph pre-training strategy alongside HAGO to collaboratively learn high-quality node representations across domains. Being compatible with various graph-based models and pre-training techniques, HAGO demonstrates broad applicability and effectiveness. Extensive experiments show that our framework outperforms state-of-the-art methods in cross-domain recommendation scenarios, underscoring its potential for real-world applications. The source code is available at https://github.com/zhy99426/HAGO.

Related papers

• Multi-Domain Graph Foundation Models: Robust Knowledge Transfer via Topology Alignment [9.215549756572976]
  Real-world graphs are often sparse and prone to noisy connections and adversarial attacks. We propose the Multi-Domain Graph Foundation Model (MDGFM), a unified framework that aligns and leverages cross-domain topological information. By aligning topologies, MDGFM not only improves multi-domain pre-training but also enables robust knowledge transfer to unseen domains.
  arXiv  Detail & Related papers  (2025-02-04T05:09:23Z)
• Gradual Domain Adaptation for Graph Learning [13.143891794601162]
  We present a graph gradual domain adaptation (GGDA) framework with the construction of a compact domain sequence.<n>Our approach starts with an efficient generation of knowledge-preserving intermediate graphs over the Fused Gromov-Wasserstein (FGW) metric.<n>Our framework concretizes the intractable inter-domain distance $W_p(mu_t,mu_t+1)$ via implementable upper and lower bounds.
  arXiv  Detail & Related papers  (2025-01-29T06:48:59Z)
• Exploiting Aggregation and Segregation of Representations for Domain Adaptive Human Pose Estimation [50.31351006532924]
  Human pose estimation (HPE) has received increasing attention recently due to its wide application in motion analysis, virtual reality, healthcare, etc. It suffers from the lack of labeled diverse real-world datasets due to the time- and labor-intensive annotation. We introduce a novel framework that capitalizes on both representation aggregation and segregation for domain adaptive human pose estimation.
  arXiv  Detail & Related papers  (2024-12-29T17:59:45Z)
• DisCo: Graph-Based Disentangled Contrastive Learning for Cold-Start Cross-Domain Recommendation [11.61586672399166]
  Cross-domain recommendation (CDR) has emerged as a promising solution. However, users with similar preferences in the source domain may exhibit different interests in the target domain. We propose a novel graph-based disentangled contrastive learning framework to capture fine-grained user intent.
  arXiv  Detail & Related papers  (2024-12-19T16:20:42Z)
• Star+: A New Multi-Domain Model for CTR Prediction [0.0]
  We introduce Star+, a novel multi-domain model for click-through rate (CTR) prediction inspired by the Star model. Our experiments on both industrial and public datasets demonstrate that Star+ significantly improves prediction accuracy and efficiency.
  arXiv  Detail & Related papers  (2024-06-24T12:03:35Z)
• FedHCDR: Federated Cross-Domain Recommendation with Hypergraph Signal Decoupling [15.159012729198619]
  We propose FedHCDR, a novel Cross-Domain Recommendation framework with Hypergraph signal decoupling. In this study, we introduce an approach called hypergraph signal decoupling (HSD) to decouple the user features into domain-exclusive and domain-shared features. Extensive experiments conducted on three real-world scenarios demonstrate that FedHCDR outperforms existing baselines significantly.
  arXiv  Detail & Related papers  (2024-03-05T03:40:39Z)
• Cross-Domain Policy Adaptation via Value-Guided Data Filtering [57.62692881606099]
  Generalizing policies across different domains with dynamics mismatch poses a significant challenge in reinforcement learning. We present the Value-Guided Data Filtering (VGDF) algorithm, which selectively shares transitions from the source domain based on the proximity of paired value targets.
  arXiv  Detail & Related papers  (2023-05-28T04:08:40Z)
• Exploiting Graph Structured Cross-Domain Representation for Multi-Domain Recommendation [71.45854187886088]
  Multi-domain recommender systems benefit from cross-domain representation learning and positive knowledge transfer. We use temporal intra- and inter-domain interactions as contextual information for our method called MAGRec. We perform experiments on publicly available datasets in different scenarios where MAGRec consistently outperforms state-of-the-art methods.
  arXiv  Detail & Related papers  (2023-02-12T19:51:32Z)
• Cross-domain recommendation via user interest alignment [20.387327479445773]
  Cross-domain recommendation aims to leverage knowledge from multiple domains to alleviate the data sparsity and cold-start problems in traditional recommender systems. The general practice of this approach is to train user embeddings in each domain separately and then aggregate them in a plain manner. We propose a novel cross-domain recommendation framework, namely COAST, to improve recommendation performance on dual domains.
  arXiv  Detail & Related papers  (2023-01-26T23:54:41Z)
• DDGHM: Dual Dynamic Graph with Hybrid Metric Training for Cross-Domain Sequential Recommendation [15.366783212837515]
  Sequential Recommendation (SR) characterizes evolving patterns of user behaviors by modeling how users transit among items. To solve this problem, we focus on Cross-Domain Sequential Recommendation (CDSR) We propose DDGHM, a novel framework for the CDSR problem, which includes two main modules, dual dynamic graph modeling and hybrid metric training.
  arXiv  Detail & Related papers  (2022-09-21T07:53:06Z)
• A cross-domain recommender system using deep coupled autoencoders [77.86290991564829]
  Two novel coupled autoencoder-based deep learning methods are proposed for cross-domain recommendation. The first method aims to simultaneously learn a pair of autoencoders in order to reveal the intrinsic representations of the items in the source and target domains. The second method is derived based on a new joint regularized optimization problem, which employs two autoencoders to generate in a deep and non-linear manner the user and item-latent factors.
  arXiv  Detail & Related papers  (2021-12-08T15:14:26Z)
• Cross-Domain Facial Expression Recognition: A Unified Evaluation Benchmark and Adversarial Graph Learning [85.6386289476598]
  We develop a novel adversarial graph representation adaptation (AGRA) framework for cross-domain holistic-local feature co-adaptation. We conduct extensive and fair evaluations on several popular benchmarks and show that the proposed AGRA framework outperforms previous state-of-the-art methods.
  arXiv  Detail & Related papers  (2020-08-03T15:00:31Z)
• Adversarial Graph Representation Adaptation for Cross-Domain Facial Expression Recognition [86.25926461936412]
  We propose a novel Adrialversa Graph Representation Adaptation (AGRA) framework that unifies graph representation propagation with adversarial learning for cross-domain holistic-local feature co-adaptation. We conduct extensive and fair experiments on several popular benchmarks and show that the proposed AGRA framework achieves superior performance over previous state-of-the-art methods.
  arXiv  Detail & Related papers  (2020-08-03T13:27:24Z)
• Learning to Combine: Knowledge Aggregation for Multi-Source Domain Adaptation [56.694330303488435]
  We propose a Learning to Combine for Multi-Source Domain Adaptation (LtC-MSDA) framework. In the nutshell, a knowledge graph is constructed on the prototypes of various domains to realize the information propagation among semantically adjacent representations. Our approach outperforms existing methods with a remarkable margin.
  arXiv  Detail & Related papers  (2020-07-17T07:52:44Z)

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.