39 citations · 103 across the 6 of their papers we have counts for
6 papers
Deconfounded Causal Collaborative Filtering
Shuyuan Xu, Juntao Tan, Shelby Heinecke +2
Recommender systems may be confounded by various types of confounding factors (also called confounders) that may lead to inaccurate recommendations and sacrificed recommendation pe…
ComFormer: Code Comment Generation via Transformer and Fusion Method-based Hybrid Code Representation
Guang Yang, Xiang Chen, Jinxin Cao +4
Developers often write low-quality code comments due to the lack of programming experience, which can reduce the efficiency of developers program comprehension. Therefore, develope…
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…
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…
Fairness-Aware Explainable Recommendation over Knowledge Graphs
Zuohui Fu, Yikun Xian, Ruoyuan Gao +8
There has been growing attention on fairness considerations recently, especially in the context of intelligent decision making systems. Explainable recommendation systems, in parti…
Learning Post-Hoc Causal Explanations for Recommendation
Shuyuan Xu, Yunqi Li, Shuchang Liu +3
State-of-the-art recommender systems have the ability to generate high-quality recommendations, but usually cannot provide intuitive explanations to humans due to the usage of blac…