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09/09/2023
RecAD: Towards A Unified Library for Recommender Attack and Defense
In recent years, recommender systems have become a ubiquitous part of ou...
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08/27/2023
Label Denoising through Cross-Model Agreement
Learning from corrupted labels is very common in real-world machine-lear...
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08/03/2023
ADRNet: A Generalized Collaborative Filtering Framework Combining Clinical and Non-Clinical Data for Adverse Drug Reaction Prediction
Adverse drug reaction (ADR) prediction plays a crucial role in both heal...
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07/19/2023
Information Retrieval Meets Large Language Models: A Strategic Report from Chinese IR Community
The research field of Information Retrieval (IR) has evolved significant...
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05/23/2023
Robust Instruction Optimization for Large Language Models with Distribution Shifts
Large Language Models have demonstrated significant ability in accomplis...
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05/03/2023
Doc2SoarGraph: Discrete Reasoning over Visually-Rich Table-Text Documents with Semantic-Oriented Hierarchical Graphs
Discrete reasoning over table-text documents (e.g., financial reports) g...
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04/11/2023
Diffusion Recommender Model
Generative models such as Generative Adversarial Networks (GANs) and Var...
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04/07/2023
Generative Recommendation: Towards Next-generation Recommender Paradigm
Recommender systems typically retrieve items from an item corpus for per...
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03/28/2023
Causal Disentangled Recommendation Against User Preference Shifts
Recommender systems easily face the issue of user preference shifts. Use...
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02/07/2023
On the Theories Behind Hard Negative Sampling for Recommendation
Negative sampling has been heavily used to train recommender models on l...
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11/27/2022
Unbiased Knowledge Distillation for Recommendation
As a promising solution for model compression, knowledge distillation (K...
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09/10/2022
Causal Intervention for Fairness in Multi-behavior Recommendation
Recommender systems usually learn user interests from various user behav...
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08/26/2022
Causal Inference in Recommender Systems: A Survey and Future Directions
Existing recommender systems extract the user preference based on learni...
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08/17/2022
CCL4Rec: Contrast over Contrastive Learning for Micro-video Recommendation
Micro-video recommender systems suffer from the ubiquitous noises in use...
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08/17/2022
Re4: Learning to Re-contrast, Re-attend, Re-construct for Multi-interest Recommendation
Effectively representing users lie at the core of modern recommender sys...
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07/25/2022
Towards Complex Document Understanding By Discrete Reasoning
Document Visual Question Answering (VQA) aims to understand visually-ric...
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07/14/2022
Explainable Sparse Knowledge Graph Completion via High-order Graph Reasoning Network
Knowledge Graphs (KGs) are becoming increasingly essential infrastructur...
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05/22/2022
GL-RG: Global-Local Representation Granularity for Video Captioning
Video captioning is a challenging task as it needs to accurately transfo...
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05/03/2022
Copy Motion From One to Another: Fake Motion Video Generation
One compelling application of artificial intelligence is to generate a v...
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04/29/2022
User-controllable Recommendation Against Filter Bubbles
Recommender systems usually face the issue of filter bubbles: overrecomm...
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04/23/2022
Reinforced Causal Explainer for Graph Neural Networks
Explainability is crucial for probing graph neural networks (GNNs), answ...
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02/01/2022
WebFormer: The Web-page Transformer for Structure Information Extraction
Structure information extraction refers to the task of extracting struct...
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01/21/2022
Deconfounding to Explanation Evaluation in Graph Neural Networks
Explainability of graph neural networks (GNNs) aims to answer “Why the G...
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01/14/2022
Training Free Graph Neural Networks for Graph Matching
We present TFGM (Training Free Graph Matching), a framework to boost the...
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12/02/2021
Learning Robust Recommender from Noisy Implicit Feedback
The ubiquity of implicit feedback makes it indispensable for building re...
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08/16/2021
Causal Incremental Graph Convolution for Recommender System Retraining
Real-world recommender system needs to be regularly retrained to keep wi...
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06/06/2021
Empowering Language Understanding with Counterfactual Reasoning
Present language understanding methods have demonstrated extraordinary a...
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06/03/2021
Deconfounded Video Moment Retrieval with Causal Intervention
We tackle the task of video moment retrieval (VMR), which aims to locali...
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05/22/2021
Deconfounded Recommendation for Alleviating Bias Amplification
Recommender systems usually amplify the biases in the data. The model le...
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05/20/2021
Probabilistic and Variational Recommendation Denoising
Learning from implicit feedback is one of the most common cases in the a...
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05/17/2021
TAT-QA: A Question Answering Benchmark on a Hybrid of Tabular and Textual Content in Finance
Hybrid data combining both tabular and textual content (e.g., financial ...
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05/13/2021
Causal Intervention for Leveraging Popularity Bias in Recommendation
Recommender system usually faces popularity bias issues: from the data p...
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10/29/2020
Model-Agnostic Counterfactual Reasoning for Eliminating Popularity Bias in Recommender System
The general aim of the recommender system is to provide personalized sug...
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10/23/2020
On the Equivalence of Decoupled Graph Convolution Network and Label Propagation
The original design of Graph Convolution Network (GCN) couples feature t...
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10/21/2020
Self-supervised Graph Learning for Recommendation
Representation learning on user-item graph for recommendation has evolve...
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10/07/2020
Bias and Debias in Recommender System: A Survey and Future Directions
While recent years have witnessed a rapid growth of research papers on r...
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09/21/2020
"Click" Is Not Equal to "Like": Counterfactual Recommendation for Mitigating Clickbait Issue
Recommendation is a prevalent and critical service in information system...
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09/11/2020
CatGCN: Graph Convolutional Networks with Categorical Node Features
Recent studies on Graph Convolutional Networks (GCNs) reveal that the in...
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06/23/2020
Data Augmentation View on Graph Convolutional Network and the Proposal of Monte Carlo Graph Learning
Today, there are two major understandings for graph convolutional networ...
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06/07/2020
Denoising Implicit Feedback for Recommendation
The ubiquity of implicit feedback makes them the default choice to build...
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05/27/2020
How to Retrain Recommender System? A Sequential Meta-Learning Method
Practical recommender systems need be periodically retrained to refresh ...
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03/05/2020
Cross-GCN: Enhancing Graph Convolutional Network with k-Order Feature Interactions
Graph Convolutional Network (GCN) is an emerging technique that performs...
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02/10/2020
Bilinear Graph Neural Network with Node Interactions
Graph Neural Network (GNN) is a powerful model to learn representations ...
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02/10/2020
Bilinear Graph Neural Network with Neighbor Interactions
Graph Neural Network (GNN) is a powerful model to learn representations ...
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05/20/2019
Neural Graph Collaborative Filtering
Learning vector representations (aka. embeddings) of users and items lie...
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02/20/2019
Graph Adversarial Training: Dynamically Regularizing Based on Graph Structure
Recent efforts show that neural networks are vulnerable to small but int...
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11/23/2018
Explicit Interaction Model towards Text Classification
Text classification is one of the fundamental tasks in natural language ...
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10/13/2018
Enhancing Stock Movement Prediction with Adversarial Training
This paper contributes a new machine learning solution for stock movemen...
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10/13/2018
Improving Stock Movement Prediction with Adversarial Training
This paper contributes a new machine learning solution for stock movemen...
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10/11/2018