Jin-Soo Ahn  Min-Cheol Park  Min-Jeong Oh  Do-Jin Choi

10.5626/JOK.2026.53.3.256

recommendation systems Graph Neural Networks Graph Attention Networks Multi-head attention

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.