5 papers
Do All Nodes Benefit Equally from Knowledge Graphs? Adaptive Node-Aware KG Fusion for Recommendation
Jaehyun Park, Minseo Jeon, Daewon Gwak +3
KG-aware recommendation has been widely studied to alleviate data sparsity by using knowledge graphs (KGs), which represent items, entities, and their relations as graphs and provi…
AlphaFree: Recommendation Free from Users, IDs, and GNNs
Minseo Jeon, Junwoo Jung, Daewon Gwak +1
Can we design effective recommender systems free from users, IDs, and GNNs? Recommender systems are central to personalized content delivery across domains, with top-K item recomme…
GDLNN: Marriage of Programming Language and Neural Networks for Accurate and Easy-to-Explain Graph Classification
Minseok Jeon, Seunghyun Park
We present GDLNN, a new graph machine learning architecture, for graph classification tasks. GDLNN combines a domain-specific programming language, called GDL, with neural networks…
Personalized Ranking on Cascading Behavior Graphs for Accurate Multi-Behavior Recommendation
Geonwoo Ko, Minseo Jeon, Jinhong Jung
Multi-behavior recommendation predicts items a user may purchase by analyzing diverse behaviors like viewing, adding to a cart, and purchasing. Existing methods fall into two categ…
Effective and Lightweight Representation Learning for Link Sign Prediction in Signed Bipartite Graphs
Gyeongmin Gu, Minseo Jeon, Hyun-Je Song +1
How can we effectively and efficiently learn node representations in signed bipartite graphs? A signed bipartite graph is a graph consisting of two nodes sets where nodes of differ…