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035			_a.b12477539
099		9	_aAIT Thesis no.DSAI-25-04
100	1		_aWut Yee Aung
245	1	0	_aA hybrid recommerder system based on LightGCN and GraphSAGE
260			_aPathum Thani, Thailand : _bAsian Institute of Technology, _c2025
300			_a66 leaves : _bill.+ _e1 online resource
490	1		_aThesis ; _vno. DSAI-25-04
500			_aA thesis submitted in partial fulfillment of the requirements for the degree of Master of Science in Data Science and Artificial Intelligence
502			_aThesis (M. Sc.) - Asian Institute of Technology, 2025
520			_aRecommender 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.
650		0	_aNeural networks (Computer science)
650		0	_aRecommender systems (Information filtering)
700	0		_aChantri Polprasert, _eChairperson
700	0		_aChaklam Silpasuwanchai, _eExamination Committee
700	0		_aMongkol Ekpanyapong, _eExamination Committee
710	2		_aAIT Scholarship, _eScholarship Donor
810	2		_aAsian Institute of Technology. _tThesis ; _vno. DSAI-25-04
856	4	0	_3Full-Text _uhttp://203.159.5.9/ait-thesis/detail.php?q=B23669
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909			_aBarcode : - _bCREATED : 2026-02-19 _cRECORD # : i13574474 _dLPATRON : 0 _eLCHKIN : - _f# RENEWALS : 0 _g# OVERDUE : 0 _hIUSE3 : 0 _iTOT CHKOUT : 0 _jTOT RENEW : 0
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