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09/18/2023
Selective Volume Mixup for Video Action Recognition
The recent advances in Convolutional Neural Networks (CNNs) and Vision T...
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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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08/23/2023
CgT-GAN: CLIP-guided Text GAN for Image Captioning
The large-scale visual-language pre-trained model, Contrastive Language-...
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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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06/07/2023
Embracing Uncertainty: Adaptive Vague Preference Policy Learning for Multi-round Conversational Recommendation
Conversational recommendation systems (CRS) effectively address informat...
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05/24/2023
How Graph Convolutions Amplify Popularity Bias for Recommendation?
Graph convolutional networks (GCNs) have become prevalent in recommender...
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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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04/06/2023
GIF: A General Graph Unlearning Strategy via Influence Function
With the greater emphasis on privacy and security in our society, the pr...
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03/15/2023
Bi-directional Distribution Alignment for Transductive Zero-Shot Learning
It is well-known that zero-shot learning (ZSL) can suffer severely from ...
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02/17/2023
Towards Fine-Grained Information: Identifying the Type and Location of Translation Errors
Fine-grained information on translation errors is helpful for the transl...
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02/09/2023
Adap-τ: Adaptively Modulating Embedding Magnitude for Recommendation
Recent years have witnessed the great successes of embedding-based metho...
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02/07/2023
FFHR: Fully and Flexible Hyperbolic Representation for Knowledge Graph Completion
Learning hyperbolic embeddings for knowledge graph (KG) has gained incre...
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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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11/05/2022
Adversarial Causal Augmentation for Graph Covariate Shift
Out-of-distribution (OOD) generalization on graphs is drawing widespread...
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10/18/2022
Alibaba-Translate China's Submission for WMT 2022 Quality Estimation Shared Task
In this paper, we present our submission to the sentence-level MQM bench...
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10/10/2022
Joint Multi-grained Popularity-aware Graph Convolution Collaborative Filtering for Recommendation
Graph Convolution Networks (GCNs), with their efficient ability to captu...
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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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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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06/23/2022
LightFR: Lightweight Federated Recommendation with Privacy-preserving Matrix Factorization
Federated recommender system (FRS), which enables many local devices to ...
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06/16/2022
Let Invariant Rationale Discovery Inspire Graph Contrastive Learning
Leading graph contrastive learning (GCL) methods perform graph augmentat...
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05/31/2022
Differentiable Invariant Causal Discovery
Learning causal structure from observational data is a fundamental chall...
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05/20/2022
GDSRec: Graph-Based Decentralized Collaborative Filtering for Social Recommendation
Generating recommendations based on user-item interactions and user-user...
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04/26/2022
Cross Pairwise Ranking for Unbiased Item Recommendation
Most recommender systems optimize the model on observed interaction data...
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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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04/20/2022
Attention in Attention: Modeling Context Correlation for Efficient Video Classification
Attention mechanisms have significantly boosted the performance of video...
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04/19/2022
Rumor Detection with Self-supervised Learning on Texts and Social Graph
Rumor detection has become an emerging and active research field in rece...
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02/22/2022
KuaiRec: A Fully-observed Dataset for Recommender Systems
Recommender systems are usually developed and evaluated on the historica...
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02/10/2022
IHGNN: Interactive Hypergraph Neural Network for Personalized Product Search
A good personalized product search (PPS) system should not only focus on...
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01/30/2022
Discovering Invariant Rationales for Graph Neural Networks
Intrinsic interpretability of graph neural networks (GNNs) is to find a ...
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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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01/07/2022
On the Effectiveness of Sampled Softmax Loss for Item Recommendation
Learning objectives of recommender models remain largely unexplored. Mos...
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12/30/2021
Deconfounded Training for Graph Neural Networks
Learning powerful representations is one central theme of graph neural n...
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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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09/27/2021
Graph Neural Networks for Recommender Systems: Challenges, Methods, and Directions
Recommender system is one of the most important information services on ...
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09/16/2021
Popularity Bias Is Not Always Evil: Disentangling Benign and Harmful Bias for Recommendation
Recommender system usually suffers from severe popularity bias – the col...
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08/22/2021
DisenKGAT: Knowledge Graph Embedding with Disentangled Graph Attention Network
Knowledge graph completion (KGC) has become a focus of attention across ...
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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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08/05/2021
Time-aware Path Reasoning on Knowledge Graph for Recommendation
Reasoning on knowledge graph (KG) has been studied for explainable recom...
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08/02/2021
Exploring Lottery Ticket Hypothesis in Media Recommender Systems
Media recommender systems aim to capture users' preferences and provide ...
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06/15/2021
User-specific Adaptive Fine-tuning for Cross-domain Recommendations
Making accurate recommendations for cold-start users has been a longstan...
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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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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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05/13/2021
Graph Learning based Recommender Systems: A Review
Recent years have witnessed the fast development of the emerging topic o...
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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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05/10/2021