A Comparative Study of GNN Architectures and Self-Supervised Objectives for Embedding Extraction in Recommender Systems - Leme, Matheus H. C.; Santos, Giovani O.; Simm, Vinicius S.; Tanno, Douglas R.; Domingues, Marcos A. (Anais do Seminário Integrado de Software e Hardware (SEMISH); 2026: Anais do LIII Seminário Integrado de Software e HardwareDO - 10.5753/semish.2026.21074, 2026)
This study systematically evaluates item embeddings generated through 16 combinations of Graph Neural Network architectures and self-supervised objectives for recommender systems. Using datasets from two distinct domains, we analyze these structural representations both in isolation and as initialization for sequential modeling. Findings reveal that embedding effectiveness is highly sensitive to domain density. Ultimately, results demonstrate that hybridizing graph-based topological information with sequential patterns significantly enhances recommendation performance and catalog coverage, reinforcing the robust synergy between structural and temporal dynamics.