Papers hypergraph embedding
“hypergraph embedding” 태그가 달린 논문 13편 · 필터 해제
Heterogeneous Hypergraph Embedding for Recommendation Systems
Recent advancements in recommender systems have focused on integrating knowledge graphs (KGs) to leverage their auxiliary information. The core idea of KG-enhanced recommenders is to incorporate rich semantic information…
hypergraph embeddingKnowledge GraphsRecommendation SystemsSelf-Supervised LearningHC-GLAD: Dual Hyperbolic Contrastive Learning for Unsupervised Graph-Level Anomaly Detection
Unsupervised graph-level anomaly detection (UGAD) has garnered increasing attention in recent years due to its significance. Most existing methods that rely on traditional GNNs mainly consider pairwise relationships betw…
Anomaly DetectionContrastive Learninghypergraph embeddingFedHCDR: Federated Cross-Domain Recommendation with Hypergraph Signal Decoupling
In recent years, Cross-Domain Recommendation (CDR) has drawn significant attention, which utilizes user data from multiple domains to enhance the recommendation performance. However, current CDR methods require sharing u…
Contrastive LearningData AugmentationFederated LearningGraph Learning+5Against Filter Bubbles: Diversified Music Recommendation via Weighted Hypergraph Embedding Learning
Recommender systems serve a dual purpose for users: sifting out inappropriate or mismatched information while accurately identifying items that align with their preferences. Numerous recommendation algorithms are designe…
Diversityhypergraph embeddingMusic RecommendationRecommendation SystemsHyCubE: Efficient Knowledge Hypergraph 3D Circular Convolutional Embedding
Knowledge hypergraph embedding models are usually computationally expensive due to the inherent complex semantic information. However, existing works mainly focus on improving the effectiveness of knowledge hypergraph em…
GPUhypergraph embeddingLGMRec: Local and Global Graph Learning for Multimodal Recommendation
The multimodal recommendation has gradually become the infrastructure of online media platforms, enabling them to provide personalized service to users through a joint modeling of user historical behaviors (e.g., purchas…
Graph EmbeddingGraph Learninghypergraph embeddingMultimodal RecommendationSpatial-Temporal Interplay in Human Mobility: A Hierarchical Reinforcement Learning Approach with Hypergraph Representation
In the realm of human mobility, the decision-making process for selecting the next-visit location is intricately influenced by a trade-off between spatial and temporal constraints, which are reflective of individual need…
Decision MakingHierarchical Reinforcement Learninghypergraph embeddingHyConvE: A Novel Embedding Model for Knowledge Hypergraph Link Prediction with Convolutional Neural Networks
Knowledge hypergraph embedding, which projects entities and n-ary relations into a low-dimensional continuous vector space to predict missing links, remains a challenging area to be explored despite the ubiquity of n-ary…
hypergraph embeddingKnowledge GraphsLink PredictionPosition+1MULTI-LEVEL APPROACH TO ACCURATE AND SCALABLE HYPERGRAPH EMBEDDING
Many problems such as node classification and link prediction in network data can be solved using graph embeddings, and a number of algorithms are known for constructing such embeddings. However, it is difficult to use g…
Graph Embeddinghypergraph embeddingLink PredictionNode ClassificationScalable Hypergraph Embedding System
Many problems such as node classification and link prediction in network data can be solved using graph embeddings. However, it is difficult to use graphs to capture non-binary relations such as communities of nodes. The…
Graph Embeddinghypergraph embeddingLink PredictionNode ClassificationKnowledge Hypergraph Embedding Meets Relational Algebra
Embedding-based methods for reasoning in knowledge hypergraphs learn a representation for each entity and relation. Current methods do not capture the procedural rules underlying the relations in the graph. We propose a …
hypergraph embeddingKnowledge GraphsLink PredictionDeep Hyperedges: a Framework for Transductive and Inductive Learning on Hypergraphs
From social networks to protein complexes to disease genomes to visual data, hypergraphs are everywhere. However, the scope of research studying deep learning on hypergraphs is still quite sparse and nascent, as there ha…
hyperedge classificationhypergraph embeddingInductive LearningNode ClassificationLearning Hypergraph-regularized Attribute Predictors
We present a novel attribute learning framework named Hypergraph-based Attribute Predictor (HAP). In HAP, a hypergraph is leveraged to depict the attribute relations in the data. Then the attribute prediction problem is …
Attributehypergraph embedding