A hybrid recommerder system based on LightGCN and GraphSAGE | |
| Author | Wut Yee Aung |
| Call Number | AIT Thesis no.DSAI-25-04 |
| Subject(s) | Neural networks (Computer science) Recommender systems (Information filtering) |
| Note | A thesis submitted in partial fulfillment of the requirements for the degree of Master of Science in Data Science and Artificial Intelligence |
| Publisher | Asian Institute of Technology |
| Abstract | 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’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. |
| Year | 2025 |
| Type | Thesis |
| School | School of Engineering and Technology |
| Department | Department of Information and Communications Technologies (DICT) |
| Academic Program/FoS | Data Science and Artificial Intelligence (DSAI) |
| Chairperson(s) | Chantri Polprasert |
| Examination Committee(s) | Chaklam Silpasuwanchai;Mongkol Ekpanyapong |
| Scholarship Donor(s) | AIT Scholarship |
| Degree | Thesis (M. Sc.) - Asian Institute of Technology, 2025 |