Search Results for author:
Found 23 papers, 8 papers with code
Few-shot Learning on Heterogeneous Graphs: Challenges, Progress, and Prospects
In this paper, we provide a comprehensive review of existing FLHG methods, covering challenges, research progress, and future prospects.
Adaptive Hypergraph Network for Trust Prediction
In this paper, we propose an Adaptive Hypergraph Network for Trust Prediction (AHNTP), a novel approach that improves trust prediction accuracy by using higher-order correlations.
Few-Shot Causal Representation Learning for Out-of-Distribution Generalization on Heterogeneous Graphs
To address this challenging problem, we propose a novel Causal OOD Heterogeneous graph Few-shot learning model, namely COHF.
Intent Contrastive Learning with Cross Subsequences for Sequential Recommendation
In this paper, Intent contrastive learning with Cross Subsequences for sequential Recommendation (ICSRec) is proposed to model users' latent intentions.
Meta-optimized Joint Generative and Contrastive Learning for Sequential Recommendation
In this paper, we propose a Meta-optimized Seq2Seq Generator and Contrastive Learning (Meta-SGCL) for sequential recommendation, which applies the meta-optimized two-step training strategy to adaptive generate contrastive views.
Cross-heterogeneity Graph Few-shot Learning
In recent years, heterogeneous graph few-shot learning has been proposed to address the label sparsity issue in heterogeneous graphs (HGs), which contain various types of nodes and edges.
Snowman: A Million-scale Chinese Commonsense Knowledge Graph Distilled from Foundation Model
Constructing commonsense knowledge graphs (CKGs) has attracted wide research attention due to its significant importance in cognitive intelligence.
Contrastive Enhanced Slide Filter Mixer for Sequential Recommendation
Sequential recommendation (SR) aims to model user preferences by capturing behavior patterns from their item historical interaction data.
Ensemble Modeling with Contrastive Knowledge Distillation for Sequential Recommendation
Our framework adopts multiple parallel networks as an ensemble of sequence encoders and recommends items based on the output distributions of all these networks.
Sequential Recommendation with Probabilistic Logical Reasoning
To better capture the uncertainty and evolution of user tastes, SR-PLR embeds users and items with a probabilistic method and conducts probabilistic logical reasoning on users' interaction patterns.
Frequency Enhanced Hybrid Attention Network for Sequential Recommendation
However, many recent studies represent that current self-attention based models are low-pass filters and are inadequate to capture high-frequency information.
Meta-optimized Contrastive Learning for Sequential Recommendation
By applying both data augmentation and learnable model augmentation operations, this work innovates the standard CL framework by contrasting data and model augmented views for adaptively capturing the informative features hidden in stochastic data augmentation.
DCMT: A Direct Entire-Space Causal Multi-Task Framework for Post-Click Conversion Estimation
In recommendation scenarios, there are two long-standing challenges, i. e., selection bias and data sparsity, which lead to a significant drop in prediction accuracy for both Click-Through Rate (CTR) and post-click Conversion Rate (CVR) tasks.
Contrastive Learning with Bidirectional Transformers for Sequential Recommendation
Contrastive learning with Transformer-based sequence encoder has gained predominance for sequential recommendation.
Few-Shot Semantic Relation Prediction across Heterogeneous Graphs
In real-world scenarios, new semantic relations constantly emerge and they typically appear with only a few labeled data.
Learnable Model Augmentation Self-Supervised Learning for Sequential Recommendation
To this end, in this paper, we propose a Learnable Model Augmentation self-supervised learning for sequential Recommendation (LMA4Rec).
Quaternion-Based Graph Convolution Network for Recommendation
Graph Convolution Network (GCN) has been widely applied in recommender systems for its representation learning capability on user and item embeddings.
Edge-Enhanced Global Disentangled Graph Neural Network for Sequential Recommendation
In this paper, we propose an Edge-Enhanced Global Disentangled Graph Neural Network (EGD-GNN) model to capture the relation information between items for global item representation and local user intention learning.
A Unified Framework for Cross-Domain and Cross-System Recommendations
Moreover, to avoid negative transfer, we further propose a Personalized training strategy to minimize the embedding difference of common entities between a richer dataset and a sparser dataset, deriving three new models, i. e., GA-DTCDR-P, GA-MTCDR-P, and GA-CDR+CSR-P, for the three scenarios respectively.
Cross-Domain Recommendation: Challenges, Progress, and Prospects
To address the long-standing data sparsity problem in recommender systems (RSs), cross-domain recommendation (CDR) has been proposed to leverage the relatively richer information from a richer domain to improve the recommendation performance in a sparser domain.
Survey and Open Problems in Privacy Preserving Knowledge Graph: Merging, Query, Representation, Completion and Applications
In this paper, to fill this gap, we summarize the open problems for privacy preserving KG in data isolation setting and propose possible solutions for them.
A Deep Framework for Cross-Domain and Cross-System Recommendations
Therefore, finding an accurate mapping of the latent factors across domains or systems is crucial to enhancing recommendation accuracy.
Leveraging Meta Information in Short Text Aggregation
Short texts such as tweets often contain insufficient word co-occurrence information for training conventional topic models.
