Papers Knowledge-Aware Recommendation
“Knowledge-Aware Recommendation” 태그가 달린 논문 14편 · 필터 해제
Hyperbolic Contrastive Learning with Model-augmentation for Knowledge-aware Recommendation
Benefiting from the effectiveness of graph neural networks (GNNs) and contrastive learning, GNN-based contrastive learning has become mainstream for knowledge-aware recommendation. However, most existing contrastive lear…
Contrastive LearningKnowledge-Aware RecommendationKnowledge GraphsComprehending Knowledge Graphs with Large Language Models for Recommender Systems
In recent years, the introduction of knowledge graphs (KGs) has significantly advanced recommender systems by facilitating the discovery of potential associations between items. However, existing methods still face sever…
Knowledge-Aware RecommendationKnowledge GraphsPrompt EngineeringReading Comprehension+4Knowledge-aware Dual-side Attribute-enhanced Recommendation
\textit{Knowledge-aware} recommendation methods (KGR) based on \textit{graph neural networks} (GNNs) and \textit{contrastive learning} (CL) have achieved promising performance. However, they fall short in modeling fine-g…
AttributeCollaborative FilteringContrastive LearningKnowledge-Aware Recommendation+1Self-Supervised Dynamic Hypergraph Recommendation based on Hyper-Relational Knowledge Graph
Knowledge graphs (KGs) are commonly used as side information to enhance collaborative signals and improve recommendation quality. In the context of knowledge-aware recommendation (KGR), graph neural networks (GNNs) have …
Collaborative FilteringKnowledge-Aware RecommendationKnowledge GraphsSelf-Supervised LearningKnowledge-refined Denoising Network for Robust Recommendation
Knowledge graph (KG), which contains rich side information, becomes an essential part to boost the recommendation performance and improve its explainability. However, existing knowledge-aware recommendation methods direc…
DenoisingKnowledge-Aware RecommendationModel OptimizationHierarchical and Contrastive Representation Learning for Knowledge-aware Recommendation
Incorporating knowledge graph into recommendation is an effective way to alleviate data sparsity. Most existing knowledge-aware methods usually perform recursive embedding propagation by enumerating graph neighbors. Howe…
Contrastive LearningKnowledge-Aware RecommendationRepresentation LearningImproving Knowledge-aware Recommendation with Multi-level Interactive Contrastive Learning
Incorporating Knowledge Graphs (KG) into recommeder system has attracted considerable attention. Recently, the technical trend of Knowledge-aware Recommendation (KGR) is to develop end-to-end models based on graph neural…
Contrastive LearningKnowledge-Aware RecommendationKnowledge GraphsRepresentation Learning+1Multi-level Cross-view Contrastive Learning for Knowledge-aware Recommender System
Knowledge graph (KG) plays an increasingly important role in recommender systems. Recently, graph neural networks (GNNs) based model has gradually become the theme of knowledge-aware recommendation (KGR). However, there …
Contrastive LearningData Augmentationgraph constructionKnowledge-Aware Recommendation+1HAKG: Hierarchy-Aware Knowledge Gated Network for Recommendation
Knowledge graph (KG) plays an increasingly important role to improve the recommendation performance and interpretability. A recent technical trend is to design end-to-end models based on information propagation schemes. …
Knowledge-Aware RecommendationRecommendation SystemsAttentive Knowledge-aware Graph Convolutional Networks with Collaborative Guidance for Personalized Recommendation
To alleviate data sparsity and cold-start problems of traditional recommender systems (RSs), incorporating knowledge graphs (KGs) to supplement auxiliary information has attracted considerable attention recently. However…
Click-Through Rate PredictionKnowledge-Aware RecommendationKnowledge GraphsRecommendation SystemsModeling Scale-free Graphs with Hyperbolic Geometry for Knowledge-aware Recommendation
Aiming to alleviate data sparsity and cold-start problems of traditional recommender systems, incorporating knowledge graphs (KGs) to supplement auxiliary information has recently gained considerable attention. Via unify…
Knowledge-Aware RecommendationKnowledge GraphsRecommendation SystemsKnowledge-guided Deep Reinforcement Learning for Interactive Recommendation
Interactive recommendation aims to learn from dynamic interactions between items and users to achieve responsiveness and accuracy. Reinforcement learning is inherently advantageous for coping with dynamic environments an…
Decision MakingDeep Reinforcement LearningInteractive RecommendationKnowledge-Aware Recommendation+4Ekar: An Explainable Method for Knowledge Aware Recommendation
This paper studies recommender systems with knowledge graphs, which can effectively address the problems of data sparsity and cold start. Recently, a variety of methods have been developed for this problem, which general…
Knowledge-Aware RecommendationKnowledge GraphsPolicy Gradient MethodsRecommendation Systems+1KB4Rec: A Dataset for Linking Knowledge Bases with Recommender Systems
To develop a knowledge-aware recommender system, a key data problem is how we can obtain rich and structured knowledge information for recommender system (RS) items. Existing datasets or methods either use side informati…
Knowledge-Aware RecommendationRecommendation Systems