Query：
学者姓名：檀彦超
Refining：
Year
Type
Indexed by
Source
Complex
Former Name
Co-
Language
Clean All
Abstract ：
Recently, heterogeneous graphs have attracted widespread attention as a powerful and practical superclass of traditional homogeneous graphs, which reflect the multi-type node entities and edge relations in the real world. Most existing methods adopt meta-path construction as the mainstream to learn long-range heterogeneous semantic messages between nodes. However, such schema constructs the node-wise correlation by connecting nodes via pre-computed fixed paths, which neglects the diversities of meta-paths on the path type and path range. In this paper, we propose a meta-path-based semantic embedding schema, which is called Heterogeneous Graph Embedding with Dual Edge Differentiation (HGE-DED) to adequately construct flexible meta-path combinations thus learning the rich and discriminative semantic of target nodes. Concretely, HGEDED devises a Multi-Type and multi-Range Meta-Path Construction (MTR-MP Construction), which covers the comprehensive exploration of meta-path combinations from path type and path range, expressing the diversity of edges at more fine-grained scales. Moreover, HGE-DED designs the semantics and meta-path joint guidance, constructing a hierarchical short- and long-range relation adjustment, which constrains the path learning as well as minimizes the impact of edge heterophily on heterogeneous graphs. Experimental results on four benchmark datasets demonstrate the effectiveness of HGE-DED compared with state-of-the-art methods.
Keyword ：
Graph neural network Graph neural network Heterogeneous information network Heterogeneous information network Meta-path combination Meta-path combination Semantic embedding Semantic embedding Semi-supervised classification Semi-supervised classification
Cite：
Copy from the list or Export to your reference management。
| GB/T 7714 | Chen, Yuhong , Chen, Fuhai , Wu, Zhihao et al. Heterogeneous Graph Embedding with Dual Edge Differentiation [J]. | NEURAL NETWORKS , 2025 , 183 . |
| MLA | Chen, Yuhong et al. "Heterogeneous Graph Embedding with Dual Edge Differentiation" . | NEURAL NETWORKS 183 (2025) . |
| APA | Chen, Yuhong , Chen, Fuhai , Wu, Zhihao , Chen, Zhaoliang , Cai, Zhiling , Tan, Yanchao et al. Heterogeneous Graph Embedding with Dual Edge Differentiation . | NEURAL NETWORKS , 2025 , 183 . |
| Export to | NoteExpress RIS BibTex |
Version ：
Abstract ：
Multi-view learning is an emerging field of multi-modal fusion, which involves representing a single instance using multiple heterogeneous features to improve compatibility prediction. However, existing graph-based multi-view learning approaches are implemented on homogeneous assumptions and pairwise relationships, which may not adequately capture the complex interactions among real-world instances. In this paper, we design a compressed hypergraph neural network from the perspective of multi-view heterogeneous graph learning. This approach effectively captures rich multi-view heterogeneous semantic information, incorporating a hypergraph structure that simultaneously enables the exploration of higher-order correlations between samples in multi-view scenarios. Specifically, we introduce efficient hypergraph convolutional networks based on an explainable regularizer-centered optimization framework. Additionally, a low-rank approximation is adopted as hypergraphs to reformat the initial complex multi-view heterogeneous graph. Extensive experiments compared with several advanced node classification methods and multi-view classification methods have demonstrated the feasibility and effectiveness of the proposed method. © 2024 Elsevier Ltd
Keyword ：
Graph neural network Graph neural network Heterogeneous graph Heterogeneous graph Hypergraph convolution Hypergraph convolution Multi-view learning Multi-view learning
Cite：
Copy from the list or Export to your reference management。
| GB/T 7714 | Huang, A. , Fang, Z. , Wu, Z. et al. Multi-view heterogeneous graph learning with compressed hypergraph neural networks [J]. | Neural Networks , 2024 , 179 . |
| MLA | Huang, A. et al. "Multi-view heterogeneous graph learning with compressed hypergraph neural networks" . | Neural Networks 179 (2024) . |
| APA | Huang, A. , Fang, Z. , Wu, Z. , Tan, Y. , Han, P. , Wang, S. et al. Multi-view heterogeneous graph learning with compressed hypergraph neural networks . | Neural Networks , 2024 , 179 . |
| Export to | NoteExpress RIS BibTex |
Version ：
Abstract ：
Cross-Domain Recommendation (CDR) have become increasingly appealing by leveraging useful information to tackle the data sparsity problem across domains. Most of latest CDR models assume that domain-shareable user-item information (e.g., rating and review on overlapped users or items) are accessible across domains. However, these assumptions become impractical due to the strict data privacy protection policy. In this paper, we propose Reducing Item Discrepancy (RidCDR) model on solving Privacy-Preserving Cross-Domain Recommendation (PPCDR) problem. Specifically, we aim to enhance the model performance on both source and target domains without overlapped users and items while protecting the data privacy. We innovatively propose private-robust embedding alignment module in RidCDR for knowledge sharing across domains while avoiding negative transfer privately. Our empirical study on Amazon and Douban datasets demonstrates that RidCDR significantly outperforms the state-of-the-art models under the PPCDR without overlapped users and items. Copyright 2024 by the author(s)
Cite：
Copy from the list or Export to your reference management。
| GB/T 7714 | Liu, W. , Zheng, X. , Chen, C. et al. Reducing Item Discrepancy via Differentially Private Robust Embedding Alignment for Privacy-Preserving Cross Domain Recommendation [未知]. |
| MLA | Liu, W. et al. "Reducing Item Discrepancy via Differentially Private Robust Embedding Alignment for Privacy-Preserving Cross Domain Recommendation" [未知]. |
| APA | Liu, W. , Zheng, X. , Chen, C. , Xu, J. , Liao, X. , Wang, F. et al. Reducing Item Discrepancy via Differentially Private Robust Embedding Alignment for Privacy-Preserving Cross Domain Recommendation [未知]. |
| Export to | NoteExpress RIS BibTex |
Version ：
Abstract ：
Cross-Domain Recommendation has been popularly studied to resolve data sparsity problem via leveraging knowledge transfer across different domains. In this paper, we focus on the Unified Cross-Domain Recommendation (Unified CDR) problem. That is, how to enhance the recommendation performance within and cross domains when users are partially overlapped. It has two main challenges, i.e., 1) how to obtain robust matching solution among the whole users and 2) how to exploit consistent and accurate results across domains. To address these two challenges, we propose MUCRP, a cross-domain recommendation framework for the Unified CDR problem. MUCRP contains three modules, i.e., variational rating reconstruction module, robust variational embedding alignment module, and cycle-consistent preference extraction module. To solve the first challenge, we propose fused Gromov-Wasserstein distribution co-clustering optimal transport to obtain more robust matching solution via considering both semantic and structure information. To tackle the second challenge, we propose embedding-consistent and prediction-consistent losses via dual autoencoder framework to achieve consistent results. Our empirical study on Douban and Amazon datasets demonstrates that MUCRP significantly outperforms the state-of-the-art models.
Keyword ：
autoencoders autoencoders cross domain recommendation cross domain recommendation domain adaptation domain adaptation Recommendation Recommendation
Cite：
Copy from the list or Export to your reference management。
| GB/T 7714 | Zheng, Xiaolin , Liu, Weiming , Chen, Chaochao et al. Mining User Consistent and Robust Preference for Unified Cross Domain Recommendation [J]. | IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING , 2024 , 36 (12) : 8758-8772 . |
| MLA | Zheng, Xiaolin et al. "Mining User Consistent and Robust Preference for Unified Cross Domain Recommendation" . | IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING 36 . 12 (2024) : 8758-8772 . |
| APA | Zheng, Xiaolin , Liu, Weiming , Chen, Chaochao , Su, Jiajie , Liao, Xinting , Hu, Mengling et al. Mining User Consistent and Robust Preference for Unified Cross Domain Recommendation . | IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING , 2024 , 36 (12) , 8758-8772 . |
| Export to | NoteExpress RIS BibTex |
Version ：
Abstract ：
Cross-Domain Recommendation (CDR) have become increasingly appealing by leveraging useful information to tackle the data sparsity problem across domains. Most of latest CDR models assume that domain-shareable user-item information (e.g., rating and review on overlapped users or items) are accessible across domains. However, these assumptions become impractical due to the strict data privacy protection policy. In this paper, we propose Reducing Item Discrepancy (RidCDR) model on solving Privacy-Preserving Cross-Domain Recommendation (PPCDR) problem. Specifically, we aim to enhance the model performance on both source and target domains without overlapped users and items while protecting the data privacy. We innovatively propose private-robust embedding alignment module in RidCDR for knowledge sharing across domains while avoiding negative transfer privately. Our empirical study on Amazon and Douban datasets demonstrates that RidCDR significantly outperforms the state-of-the-art models under the PPCDR without overlapped users and items. Copyright 2024 by the author(s)
Keyword ：
Differential privacy Differential privacy Privacy-preserving techniques Privacy-preserving techniques
Cite：
Copy from the list or Export to your reference management。
| GB/T 7714 | Liu, Weiming , Zheng, Xiaolin , Chen, Chaochao et al. Reducing Item Discrepancy via Differentially Private Robust Embedding Alignment for Privacy-Preserving Cross Domain Recommendation [C] . 2024 : 32455-32470 . |
| MLA | Liu, Weiming et al. "Reducing Item Discrepancy via Differentially Private Robust Embedding Alignment for Privacy-Preserving Cross Domain Recommendation" . (2024) : 32455-32470 . |
| APA | Liu, Weiming , Zheng, Xiaolin , Chen, Chaochao , Xu, Jiahe , Liao, Xinting , Wang, Fan et al. Reducing Item Discrepancy via Differentially Private Robust Embedding Alignment for Privacy-Preserving Cross Domain Recommendation . (2024) : 32455-32470 . |
| Export to | NoteExpress RIS BibTex |
Version ：
Abstract ：
Recently, dual-target cross-domain recommendation (DTCDR) has been proposed to alleviate the data sparsity problem by sharing the common knowledge across domains simultaneously.However, existing methods often assume that personal data containing abundant identifiable information can be directly accessed, which results in a controversial privacy leakage problem of DTCDR.To this end, we introduce the P2DTR framework, a novel approach in DTCDR while protecting private user information.Specifically, we first design a novel inter-client knowledge extraction mechanism, which exploits the private set intersection algorithm and prototype-based federated learning to enable collaboratively modeling among multiple users and a server.Furthermore, to improve the recommendation performance based on the extracted common knowledge across domains, we proposed an intra-client enhanced recommendation, consisting of a constrained dominant set (CDS) propagation mechanism and dual-recommendation module.Extensive experiments on real-world datasets validate that our proposed P2DTR framework achieves superior utility under a privacy-preserving guarantee on both domains. © 2024 International Joint Conferences on Artificial Intelligence. All rights reserved.
Cite：
Copy from the list or Export to your reference management。
| GB/T 7714 | Lin, Zhenghong , Huang, Wei , Zhang, Hengyu et al. Enhancing Dual-Target Cross-Domain Recommendation with Federated Privacy-Preserving Learning [C] . 2024 : 2153-2161 . |
| MLA | Lin, Zhenghong et al. "Enhancing Dual-Target Cross-Domain Recommendation with Federated Privacy-Preserving Learning" . (2024) : 2153-2161 . |
| APA | Lin, Zhenghong , Huang, Wei , Zhang, Hengyu , Xu, Jiayu , Liu, Weiming , Liao, Xinting et al. Enhancing Dual-Target Cross-Domain Recommendation with Federated Privacy-Preserving Learning . (2024) : 2153-2161 . |
| Export to | NoteExpress RIS BibTex |
Version ：
Abstract ：
With the rapid growth of activities on the web, large amounts of interaction data on multimedia platforms are easily accessible, including e-commerce, music sharing, and social media. By discovering various interests of users, recommender systems can improve user satisfaction without accessing overwhelming personal information. Compared to graph-based models, hypergraph-based collaborative filtering has the ability to model higher-order relations besides pair-wise relations among users and items, where the hypergraph structures are mainly obtained from specialized data or external knowledge. However, the above well-constructed hypergraph structures are often not readily available in every situation. To this end, we first propose a novel framework named HGRec, which can enhance recommendation via automatic hypergraph generation. By exploiting the clustering mechanism based on the user/item similarity, we group users and items without additional knowledge for hypergraph structure learning and design a cross-view recommendation module to alleviate the combinatorial gaps between the representations of the local ordinary graph and the global hypergraph. Furthermore, we devise a sparse optimization strategy to ensure the effectiveness of hypergraph structures, where a novel integration of the l( 2,1)-norm and optimal transport framework is designed for hypergraph generation. We term the model HGRec with sparse optimization strategy as HGRec++. Extensive experiments on public multi-domain datasets demonstrate the superiority brought by our HGRec++, which gains average 8.1% and 9.8% improvement over state-of-the-art baselines regarding Recall and NDCG metrics, respectively.
Keyword ：
graph convolutional network graph convolutional network hypergraph generation hypergraph generation Recommender systems Recommender systems sparse optimization sparse optimization
Cite：
Copy from the list or Export to your reference management。
| GB/T 7714 | Lin, Zhenghong , Yan, Qishan , Liu, Weiming et al. Automatic Hypergraph Generation for Enhancing Recommendation With Sparse Optimization [J]. | IEEE TRANSACTIONS ON MULTIMEDIA , 2024 , 26 : 5680-5693 . |
| MLA | Lin, Zhenghong et al. "Automatic Hypergraph Generation for Enhancing Recommendation With Sparse Optimization" . | IEEE TRANSACTIONS ON MULTIMEDIA 26 (2024) : 5680-5693 . |
| APA | Lin, Zhenghong , Yan, Qishan , Liu, Weiming , Wang, Shiping , Wang, Menghan , Tan, Yanchao et al. Automatic Hypergraph Generation for Enhancing Recommendation With Sparse Optimization . | IEEE TRANSACTIONS ON MULTIMEDIA , 2024 , 26 , 5680-5693 . |
| Export to | NoteExpress RIS BibTex |
Version ：
Abstract ：
With the rapid development of Internet and Web techniques, Cross-Domain Recommendation (CDR) models have been widely explored for resolving the data-sparsity and cold-start problem. Meanwhile, most CDR models should utilize explicit domain-shareable information (e.g., overlapped users or items) for knowledge transfer across domains. However, this assumption may not be always satisfied since users and items are always non-overlapped in real practice. The performance of many previous works will be severely impaired when these domain-shareable information are not available. To address the aforementioned issues, we propose the Joint Preference Exploration and Dynamic Embedding Transportation model (JPEDET) in this paper which is a novel framework for solving the CDR problem when users and items are non-overlapped. JPEDET includes two main modules, i.e., joint preference exploration module and dynamic embedding transportation module. The joint preference exploration module aims to fuse rating and review information for modelling user preferences. The dynamic embedding transportation module is set to share knowledge via neural ordinary equations for dual transformation across domains. Moreover, we innovatively propose the dynamic transport flow equipped with linear interpolation guidance on barycentric Wasserstein path for achieving accurate and bidirectional transformation. Our empirical study on Amazon datasets demonstrates that JPEDET outperforms the state-of-the-art models under the CDR setting. Copyright © 2024, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved.
Keyword ：
Artificial intelligence Artificial intelligence Clock and data recovery circuits (CDR circuits) Clock and data recovery circuits (CDR circuits) Embeddings Embeddings Information dissemination Information dissemination Knowledge management Knowledge management Linear transformations Linear transformations User profile User profile
Cite：
Copy from the list or Export to your reference management。
| GB/T 7714 | Liu, Weiming , Chen, Chaochao , Liao, Xinting et al. Learning Accurate and Bidirectional Transformation via Dynamic Embedding Transportation for Cross-Domain Recommendation [C] . 2024 : 8815-8823 . |
| MLA | Liu, Weiming et al. "Learning Accurate and Bidirectional Transformation via Dynamic Embedding Transportation for Cross-Domain Recommendation" . (2024) : 8815-8823 . |
| APA | Liu, Weiming , Chen, Chaochao , Liao, Xinting , Hu, Mengling , Tan, Yanchao , Wang, Fan et al. Learning Accurate and Bidirectional Transformation via Dynamic Embedding Transportation for Cross-Domain Recommendation . (2024) : 8815-8823 . |
| Export to | NoteExpress RIS BibTex |
Version ：
Abstract ：
Estimating individual treatment effects (ITE) from observational data is challenging due to the absence of counterfactuals and the treatment selection bias. Prevalent ITE estimation methods tackle these challenges by aligning the treated and controlled distributions in the representational space. However, two critical issues have long been overlooked: (1)Mini-batch sampling sensitivity (MSS) issue, where representation distribution alignment at a mini-batch level is vulnerable to poor sampling cases, such as data imbalance and outliers; (2)Inconsistent representation learning (IRL) issue, where representation learning within a unified backbone network suffers from inconsistent gradient update directions due to the distribution skew between different treatment groups. To resolve these issues, we propose CE-RCFR, a Robust CounterFactual Regression framework for Consensus-Enabled causal effect estimation, including a relaxed distribution discrepancy regularizer (RDDR) module and a consensus-enabled aggregator (CEA) module. Specifically, for the robust representation alignment perspective, RDDR addresses the MSS issue by minimizing unbalanced optimal transport divergence between different treatment groups with a relaxed marginal constraint. For the accurate representation optimization perspective, CEA addresses the IRL issue by resolving the consistent gradient update directions on shared parameters within the backbone network. Extensive experiments demonstrate that CE-RCFR significantly outperforms the state-of-the-art methods in treatment effect estimations. © 2024 Copyright held by the owner/author(s). Publication rights licensed to ACM.
Keyword ：
counterfactual inference counterfactual inference representation learning representation learning treatment effect estimation treatment effect estimation treatment selection bias treatment selection bias
Cite：
Copy from the list or Export to your reference management。
| GB/T 7714 | Wang, F. , Chen, C. , Liu, W. et al. CE-RCFR: Robust Counterfactual Regression for Consensus-Enabled Treatment Effect Estimation [未知]. |
| MLA | Wang, F. et al. "CE-RCFR: Robust Counterfactual Regression for Consensus-Enabled Treatment Effect Estimation" [未知]. |
| APA | Wang, F. , Chen, C. , Liu, W. , Fan, T. , Liao, X. , Tan, Y. et al. CE-RCFR: Robust Counterfactual Regression for Consensus-Enabled Treatment Effect Estimation [未知]. |
| Export to | NoteExpress RIS BibTex |
Version ：
Abstract ：
The sparse interactions between users and items have aggravated the difficulty of their representations in recommender systems. Existing methods leverage tags to alleviate the sparsity problem but ignore prevalent logical relations among items and tags (e.g., membership, hierarchy, and exclusion), which can be leveraged to enhance the accuracy of modeling user preferences and conducting recommendations. To this end, we propose to extract logical relations among item tags from existing tag taxonomies and exploit the individual strengths of the Poincaré and the Lorentz models in hyperbolic space for logical relation modeling towards enhanced recommendations. Moreover, we find that the logical relations directly extracted from existing tag taxonomies can be inaccurate and coarse. Therefore, we further devise innovative consistency-based and granularity-based weighting mechanisms based on user behavior patterns for data-driven logical relation mining that can be jointly optimized along with recommendations in an end-to-end fashion. Extensive experiments on four real-world benchmark datasets show drastic performance gains brought by our proposed framework, which constantly achieves an average of 8.25% improvement over state-of-the-art competitors regarding both Recall and NDCG metrics. Insightful case studies further demonstrate that our automatically refined logical relations are highly accurate and interpretable. © 2024 IEEE.
Keyword ：
Hyperbolic space Hyperbolic space Logical relations Logical relations Recommender systems Recommender systems
Cite：
Copy from the list or Export to your reference management。
| GB/T 7714 | Tan, Y. , Lv, H. , Zhou, Z. et al. Logical Relation Modeling and Mining in Hyperbolic Space for Recommendation [未知]. |
| MLA | Tan, Y. et al. "Logical Relation Modeling and Mining in Hyperbolic Space for Recommendation" [未知]. |
| APA | Tan, Y. , Lv, H. , Zhou, Z. , Guo, W. , Xiong, B. , Liu, W. et al. Logical Relation Modeling and Mining in Hyperbolic Space for Recommendation [未知]. |
| Export to | NoteExpress RIS BibTex |
Version ：
Export
| Results： |
Selected to |
| Format： |
