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03/23/2023
Uncertainty Calibration for Counterfactual Propensity Estimation in Recommendation
In recommendation systems, a large portion of the ratings are missing du...
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02/02/2023
MetaTKG: Learning Evolutionary Meta-Knowledge for Temporal Knowledge Graph Reasoning
Reasoning over Temporal Knowledge Graphs (TKGs) aims to predict future f...
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10/22/2022
The Devil is in the Conflict: Disentangled Information Graph Neural Networks for Fraud Detection
Graph-based fraud detection has heretofore received considerable attenti...
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09/29/2022
Improving Molecular Pretraining with Complementary Featurizations
Molecular pretraining, which learns molecular representations over massi...
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03/13/2022
A Survey on Deep Graph Generation: Methods and Applications
Graphs are ubiquitous in encoding relational information of real-world o...
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10/14/2021
Relation-aware Heterogeneous Graph for User Profiling
User profiling has long been an important problem that investigates user...
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09/02/2021
An Empirical Study of Graph Contrastive Learning
Graph Contrastive Learning (GCL) establishes a new paradigm for learning...
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08/31/2021
Structure-Aware Hard Negative Mining for Heterogeneous Graph Contrastive Learning
Recently, heterogeneous Graph Neural Networks (GNNs) have become a de fa...
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08/16/2021
Deep Contrastive Learning for Multi-View Network Embedding
Multi-view network embedding aims at projecting nodes in the network to ...
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08/15/2021
Deep Active Learning for Text Classification with Diverse Interpretations
Recently, Deep Neural Networks (DNNs) have made remarkable progress for ...
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08/10/2021
Fully Hyperbolic Graph Convolution Network for Recommendation
Recently, Graph Convolution Network (GCN) based methods have achieved ou...
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08/10/2021
Label-informed Graph Structure Learning for Node Classification
Graph Neural Networks (GNNs) have achieved great success among various d...
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05/25/2021
GraphFM: Graph Factorization Machines for Feature Interaction Modeling
Factorization machine (FM) is a prevalent approach to modeling pairwise ...
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05/22/2021
Explainable Enterprise Credit Rating via Deep Feature Crossing Network
Due to the powerful learning ability on high-rank and non-linear feature...
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04/26/2021
Represent Items by Items: An Enhanced Representation of the Target Item for Recommendation
Item-based collaborative filtering (ICF) has been widely used in industr...
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04/19/2021
Mining Latent Structures for Multimedia Recommendation
Multimedia content is of predominance in the modern Web era. Investigati...
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04/15/2021
Dynamic Graph Neural Networks for Sequential Recommendation
Modeling users' preference from his historical sequences is one of the c...
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04/07/2021
DyGCN: Dynamic Graph Embedding with Graph Convolutional Network
Graph embedding, aiming to learn low-dimensional representations (aka. e...
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03/29/2021
Graph Classification by Mixture of Diverse Experts
Graph classification is a challenging research problem in many applicati...
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03/04/2021
Deep Graph Structure Learning for Robust Representations: A Survey
Graph Neural Networks (GNNs) are widely used for analyzing graph-structu...
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02/22/2021
Graph-based Hierarchical Relevance Matching Signals for Ad-hoc Retrieval
The ad-hoc retrieval task is to rank related documents given a query and...
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01/28/2021
A Graph-based Relevance Matching Model for Ad-hoc Retrieval
To retrieve more relevant, appropriate and useful documents given a quer...
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01/11/2021
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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01/08/2021
Dynamic Graph Collaborative Filtering
Dynamic recommendation is essential for modern recommender systems to pr...
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12/10/2020
Cold-start Sequential Recommendation via Meta Learner
This paper explores meta-learning in sequential recommendation to allevi...
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11/13/2020
Heterogeneous Graph Collaborative Filtering
Graph-based collaborative filtering (CF) algorithms have gained increasi...
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10/30/2020
Deep Active Graph Representation Learning
Graph neural networks (GNNs) aim to learn graph representations that pre...
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10/27/2020
Graph Contrastive Learning with Adaptive Augmentation
Recently, contrastive learning (CL) has emerged as a successful method f...
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09/21/2020
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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09/03/2020
CAGNN: Cluster-Aware Graph Neural Networks for Unsupervised Graph Representation Learning
Unsupervised graph representation learning aims to learn low-dimensional...
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08/27/2020
One Shot 3D Photography
3D photography is a new medium that allows viewers to more fully experie...
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08/17/2020
Disentangled Item Representation for Recommender Systems
Item representations in recommendation systems are expected to reveal th...
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06/29/2020
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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06/07/2020
Deep Graph Contrastive Representation Learning
Graph representation learning nowadays becomes fundamental in analyzing ...
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05/06/2020
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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04/22/2020
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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11/26/2019
Independence Promoted Graph Disentangled Networks
We address the problem of disentangled representation learning with inde...
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11/11/2019
Learning Preferences and Demands in Visual Recommendation
Visual information is an important factor in recommender systems, in whi...
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11/05/2019
GraphAIR: Graph Representation Learning with Neighborhood Aggregation and Interaction
Graph representation learning is of paramount importance for a variety o...
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10/20/2019
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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10/12/2019
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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07/31/2019
Semi-supervised Compatibility Learning Across Categories for Clothing Matching
Learning the compatibility between fashion items across categories is a ...
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02/26/2019
Learning Vertex Convolutional Networks for Graph Classification
In this paper, we develop a new aligned vertex convolutional network mod...
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02/21/2019
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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02/13/2019
Semi-supervised Node Classification via Hierarchical Graph Convolutional Networks
Graph convolutional networks (GCNs) have been successfully applied in no...
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11/01/2018
Session-based Recommendation with Graph Neural Networks
The problem of session-based recommendation aims to predict users' actio...
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11/14/2017
A Hierarchical Contextual Attention-based GRU Network for Sequential Recommendation
Sequential recommendation is one of fundamental tasks for Web applicatio...
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09/29/2016
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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09/19/2016
Context-aware Sequential Recommendation
Since sequential information plays an important role in modeling user be...
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07/21/2016