TY  - JOUR
T1  - Recommendation Systems based on Feature-Adaptive Graph Attention Network
AU  - Ahn, Jin-Soo 
AU  - Park, Min-Cheol 
AU  - Oh, Min-Jeong 
AU  - Choi, Do-Jin 
JO  - Journal of KIISE, JOK
PY  - 2026
DA  - 2026/1/14
DO  - 10.5626/JOK.2026.53.3.256
KW  - recommendation systems
KW  - graph neural networks
KW  - graph attention networks
KW  - multi-head attention
AB  - With the rapid advancement of information and communication technology, the volume and variety of online content have grown explosively, causing users to suffer from information overload and increasing the need for effective recommender systems. In this paper, we propose a graph learning method that models user–content interactions as a graph and effectively captures domain-specific structural characteristics and noise. To achieve data-adaptive graph learning, we build on an enhanced Graph Attention Network (GAT) that applies different operations to each head and design specialized heads such as RatingConv and PopConv to reflect data characteristics during training. The proposed method achieves approximately a 10% performance improvement in the NDCG@20 metric compared to existing baseline models on standard recommendation benchmarks, including the MovieLens-1M, MovieLens-25M, and FilmTrust datasets.