TY  - BOOK
AU  - Wut Yee Aung
AU  - Chantri Polprasert,
AU  - Chaklam Silpasuwanchai,
AU  - Mongkol Ekpanyapong,
ED  - AIT Scholarship,
TI  - A hybrid recommerder system based on LightGCN and GraphSAGE
T2  - Thesis
PY  - 2025///
CY  - Pathum Thani, Thailand
PB  - Asian Institute of Technology
KW  - Neural networks (Computer science)
KW  - Recommender systems (Information filtering)
N1  - A thesis submitted in partial fulfillment of the requirements for the  degree of Master of Science in Data Science and Artificial Intelligence; Thesis (M. Sc.) - Asian Institute of Technology, 2025
N2  - Recommender systems (RS) are essential for delivering personalized recommendations, enhancing user experiences, and increasing business profitability. Traditional RS methods, such  as collaborative filtering, content-based filtering, and hybrid methods, offer unique benefits but  also face certain constraints. Collaborative filtering, exemplified by LightGCN, excels at capturing topological patterns from user-item interactions but struggles with limited user-item interactions and cold-start problems. Content-based filtering mitigates item cold-start issues by leveraging item features but overlooks collaborative preferences from similar users. To address  these challenges, this study proposes GSAGE-LGCN, a hybrid model integrating GraphSAGE  with LightGCN. GraphSAGE enhances node embeddings through inductive feature learning  from sampled neighbors, complementing LightGCN{u2019}s collaborative filtering capabilities. Three  variants (V1, V2, V3) were developed to explore different integration strategies, evaluated on  MovieLens-100K, MovieLens-1Mil, Brazilian E-Commerce, and Amazon Book 2018 datasets.  Results show that GSAGE-LGCN V1 significantly improves performance in low-density and cold-start scenarios, achieving a 62.1% and 97.6% increase in Precision@20 on MovieLens 100K and MovieLens-1Mil datasets respectively under user cold-start conditions. In the very  low density dataset, Brazilian E-Commerce, V1 achieves a 96.3% increase in Precision@20 in  the cold-start scenario. This research highlights the potential of combining GraphSAGE and LightGCN to address the issues arising from limited user-item interactions and cold-start prob lems, offering a robust hybrid recommender system for diverse recommendation scenarios
UR  - http://203.159.5.9/ait-thesis/detail.php?q=B23669
ER  -