5 citations · 9 across the 4 of their papers we have counts for
4 papers
Non-parametric Graph Convolution for Re-ranking in Recommendation Systems
Zhongyu Ouyang, Mingxuan Ju, Soroush Vosoughi +1
Graph knowledge has been proven effective in enhancing item rankings in recommender systems (RecSys), particularly during the retrieval stage. However, its application in the ranki…
MOPI-HFRS: A Multi-objective Personalized Health-aware Food Recommendation System with LLM-enhanced Interpretation
Zheyuan Zhang, Zehong Wang, Tianyi Ma +8
The prevalence of unhealthy eating habits has become an increasingly concerning issue in the United States. However, major food recommendation platforms (e.g., Yelp) continue to pr…
How Does Message Passing Improve Collaborative Filtering?
Mingxuan Ju, William Shiao, Zhichun Guo +4
Collaborative filtering (CF) has exhibited prominent results for recommender systems and been broadly utilized for real-world applications. A branch of research enhances CF methods…
GraphPatcher: Mitigating Degree Bias for Graph Neural Networks via Test-time Augmentation
Mingxuan Ju, Tong Zhao, Wenhao Yu +2
Recent studies have shown that graph neural networks (GNNs) exhibit strong biases towards the node degree: they usually perform satisfactorily on high-degree nodes with rich neighb…