440 citations · 1.1k across the 26 of their papers we have counts for
9 papers · 1 filter
D-HYPR: Harnessing Neighborhood Modeling and Asymmetry Preservation for Digraph Representation Learning
Honglu Zhou, Advith Chegu, Samuel S. Sohn +3
Digraph Representation Learning (DRL) aims to learn representations for directed homogeneous graphs (digraphs). Prior work in DRL is largely constrained (e.g., limited to directed…
Dense Contrastive Visual-Linguistic Pretraining
Lei Shi, Kai Shuang, Shijie Geng +5
Inspired by the success of BERT, several multimodal representation learning approaches have been proposed that jointly represent image and text. These approaches achieve superior p…
User-oriented Fairness in Recommendation
Yunqi Li, Hanxiong Chen, Zuohui Fu +2
As a highly data-driven application, recommender systems could be affected by data bias, resulting in unfair results for different data groups, which could be a reason that affects…
Faithfully Explainable Recommendation via Neural Logic Reasoning
Yaxin Zhu, Yikun Xian, Zuohui Fu +2
Knowledge graphs (KG) have become increasingly important to endow modern recommender systems with the ability to generate traceable reasoning paths to explain the recommendation pr…
Efficient Non-Sampling Knowledge Graph Embedding
Zelong Li, Jianchao Ji, Zuohui Fu +4
Knowledge Graph (KG) is a flexible structure that is able to describe the complex relationship between data entities. Currently, most KG embedding models are trained based on negat…
Context-Aware Interaction Network for Question Matching
Zhe Hu, Zuohui Fu, Yu Yin +1
Impressive milestones have been achieved in text matching by adopting a cross-attention mechanism to capture pertinent semantic connections between two sentence representations. Ho…
Causal Collaborative Filtering
Shuyuan Xu, Yingqiang Ge, Yunqi Li +3
Many of the traditional recommendation algorithms are designed based on the fundamental idea of mining or learning correlative patterns from data to estimate the user-item correlat…
RomeBERT: Robust Training of Multi-Exit BERT
Shijie Geng, Peng Gao, Zuohui Fu +1
BERT has achieved superior performances on Natural Language Understanding (NLU) tasks. However, BERT possesses a large number of parameters and demands certain resources to deploy.…
Discrete Knowledge Graph Embedding based on Discrete Optimization
Yunqi Li, Shuyuan Xu, Bo Liu +4
This paper proposes a discrete knowledge graph (KG) embedding (DKGE) method, which projects KG entities and relations into the Hamming space based on a computationally tractable di…