03293nam a2200361 a 4500 20260817161320.0 260219s20259999th mm 000 0 eng d .b12477539 AIT Thesis no.DSAI-25-04 Wut Yee Aung A hybrid recommerder system based on LightGCN and GraphSAGE Pathum Thani, Thailand : Asian Institute of Technology, 2025 66 leaves : ill.+ 1 online resource Thesis ; no. DSAI-25-04 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 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. Neural networks (Computer science) Recommender systems (Information filtering) Chantri Polprasert, Chairperson Chaklam Silpasuwanchai, Examination Committee Mongkol Ekpanyapong, Examination Committee AIT Scholarship, Scholarship Donor Asian Institute of Technology. Thesis ; no. DSAI-25-04 Full-Text http://203.159.5.9/ait-thesis/detail.php?q=B23669 .b12477539 mnarc a 260310 0 260309 m h a 0 mnarc 67 Barcode : - CREATED : 2026-02-19 RECORD # : i13574474 LPATRON : 0 LCHKIN : - # RENEWALS : 0 # OVERDUE : 0 IUSE3 : 0 TOT CHKOUT : 0 TOT RENEW : 0 824 824 0 0 0 0 MAIN MAIN mnarc 2026-08-17 0 AIT Thesis no.DSAI-25-04 2026-08-17 16:13:20 1 2026-08-17 67