20 citations · 47 across the 4 of their papers we have counts for
4 papers
Toward a Better Understanding of Loss Functions for Collaborative Filtering
Seongmin Park, Mincheol Yoon, Jae-woong Lee +2
Collaborative filtering (CF) is a pivotal technique in modern recommender systems. The learning process of CF models typically consists of three components: interaction encoder, lo…
Generating Post-hoc Explanations for Skip-gram-based Node Embeddings by Identifying Important Nodes with Bridgeness
Hogun Park, Jennifer Neville
Node representation learning in a network is an important machine learning technique for encoding relational information in a continuous vector space while preserving the inherent…
Incorporating Experts' Judgment into Machine Learning Models
Hogun Park, Aly Megahed, Peifeng Yin +3
Machine learning (ML) models have been quite successful in predicting outcomes in many applications. However, in some cases, domain experts might have a judgment about the expected…
Dual Policy Learning for Aggregation Optimization in Graph Neural Network-based Recommender Systems
Heesoo Jung, Sangpil Kim, Hogun Park
Graph Neural Networks (GNNs) provide powerful representations for recommendation tasks. GNN-based recommendation systems capture the complex high-order connectivity between users a…