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Disentangled Self-Attentive Neural Networks for Click-Through Rate Prediction
Click-through rate (CTR) prediction, which aims to predict the probabili...
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Dynamic Graph Collaborative Filtering
Dynamic recommendation is essential for modern recommender systems to pr...
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Cold-start Sequential Recommendation via Meta Learner
This paper explores meta-learning in sequential recommendation to allevi...
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Heterogeneous Graph Collaborative Filtering
Graph-based collaborative filtering (CF) algorithms have gained increasi...
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Deep Active Graph Representation Learning
Graph neural networks (GNNs) aim to learn graph representations that pre...
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Graph Contrastive Learning with Adaptive Augmentation
Recently, contrastive learning (CL) has emerged as a successful method f...
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DGTN: Dual-channel Graph Transition Network for Session-based Recommendation
The task of session-based recommendation is to predict user actions base...
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CAGNN: Cluster-Aware Graph Neural Networks for Unsupervised Graph Representation Learning
Unsupervised graph representation learning aims to learn low-dimensional...
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One Shot 3D Photography
3D photography is a new medium that allows viewers to more fully experie...
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Disentangled Item Representation for Recommender Systems
Item representations in recommendation systems are expected to reveal th...
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TFNet: Multi-Semantic Feature Interaction for CTR Prediction
The CTR (Click-Through Rate) prediction plays a central role in the doma...
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Deep Graph Contrastive Representation Learning
Graph representation learning nowadays becomes fundamental in analyzing ...
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TAGNN: Target Attentive Graph Neural Networks for Session-based Recommendation
Session-based recommendation nowadays plays a vital role in many website...
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Every Document Owns Its Structure: Inductive Text Classification via Graph Neural Networks
Text classification is fundamental in natural language processing (NLP),...
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Independence Promoted Graph Disentangled Networks
We address the problem of disentangled representation learning with inde...
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Learning Preferences and Demands in Visual Recommendation
Visual information is an important factor in recommender systems, in whi...
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GraphAIR: Graph Representation Learning with Neighborhood Aggregation and Interaction
Graph representation learning is of paramount importance for a variety o...
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Personalizing Graph Neural Networks with Attention Mechanism for Session-based Recommendation
The problem of personalized session-based recommendation aims to predict...
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Fi-GNN: Modeling Feature Interactions via Graph Neural Networks for CTR Prediction
Click-through rate (CTR) prediction is an essential task in web applicat...
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Semi-supervised Compatibility Learning Across Categories for Clothing Matching
Learning the compatibility between fashion items across categories is a ...
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Learning Vertex Convolutional Networks for Graph Classification
In this paper, we develop a new aligned vertex convolutional network mod...
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Dressing as a Whole: Outfit Compatibility Learning Based on Node-wise Graph Neural Networks
With the rapid development of fashion market, the customers' demands of ...
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Semi-supervised Node Classification via Hierarchical Graph Convolutional Networks
Graph convolutional networks (GCNs) have been successfully applied in no...
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Session-based Recommendation with Graph Neural Networks
The problem of session-based recommendation aims to predict users' actio...
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A Hierarchical Contextual Attention-based GRU Network for Sequential Recommendation
Sequential recommendation is one of fundamental tasks for Web applicatio...
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ICE: Information Credibility Evaluation on Social Media via Representation Learning
With the rapid growth of social media, rumors are also spreading widely ...
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Context-aware Sequential Recommendation
Since sequential information plays an important role in modeling user be...
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A Comprehensive Survey on Cross-modal Retrieval
In recent years, cross-modal retrieval has drawn much attention due to t...
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