AI SearchA hybrid recommerder system based on LightGCN and GraphSAGE (Record no. 824)
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| fixed length control field | 03293nam a2200361 a 4500 |
| 005 - DATE AND TIME OF LATEST TRANSACTION | |
| control field | 20260817161320.0 |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION | |
| fixed length control field | 260219s20259999th mm 000 0 eng d |
| 035 ## - SYSTEM CONTROL NUMBER | |
| System control number | .b12477539 |
| 099 #9 - LOCAL FREE-TEXT CALL NUMBER (OCLC) | |
| Classification number | AIT Thesis no.DSAI-25-04 |
| 100 1# - MAIN ENTRY--PERSONAL NAME | |
| Personal name | Wut Yee Aung |
| 245 10 - TITLE STATEMENT | |
| Title | A hybrid recommerder system based on LightGCN and GraphSAGE |
| 260 ## - PUBLICATION, DISTRIBUTION, ETC. | |
| Place of publication, distribution, etc. | Pathum Thani, Thailand : |
| Name of publisher, distributor, etc. | Asian Institute of Technology, |
| Date of publication, distribution, etc. | 2025 |
| 300 ## - PHYSICAL DESCRIPTION | |
| Extent | 66 leaves : |
| Other physical details | ill.+ |
| Accompanying material | 1 online resource |
| 490 1# - SERIES STATEMENT | |
| Series statement | Thesis ; |
| Volume/sequential designation | no. DSAI-25-04 |
| 500 ## - GENERAL NOTE | |
| General note | A thesis submitted in partial fulfillment of the requirements for the degree of Master of Science in Data Science and Artificial Intelligence |
| 502 ## - DISSERTATION NOTE | |
| Dissertation note | Thesis (M. Sc.) - Asian Institute of Technology, 2025 |
| 520 ## - SUMMARY, ETC. | |
| Summary, etc. | 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. |
| 650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical term or geographic name entry element | Neural networks (Computer science) |
| 650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical term or geographic name entry element | Recommender systems (Information filtering) |
| 700 0# - ADDED ENTRY--PERSONAL NAME | |
| Personal name | Chantri Polprasert, |
| Relator term | Chairperson |
| 700 0# - ADDED ENTRY--PERSONAL NAME | |
| Personal name | Chaklam Silpasuwanchai, |
| Relator term | Examination Committee |
| 700 0# - ADDED ENTRY--PERSONAL NAME | |
| Personal name | Mongkol Ekpanyapong, |
| Relator term | Examination Committee |
| 710 2# - ADDED ENTRY--CORPORATE NAME | |
| Corporate name or jurisdiction name as entry element | AIT Scholarship, |
| Relator term | Scholarship Donor |
| 810 2# - SERIES ADDED ENTRY--CORPORATE NAME | |
| Corporate name or jurisdiction name as entry element | Asian Institute of Technology. |
| Title of a work | Thesis ; |
| Volume/sequential designation | no. DSAI-25-04 |
| 856 40 - ELECTRONIC LOCATION AND ACCESS | |
| Materials specified | Full-Text |
| Uniform Resource Identifier | <a href="http://203.159.5.9/ait-thesis/detail.php?q=B23669">http://203.159.5.9/ait-thesis/detail.php?q=B23669</a> |
| 907 ## - LOCAL DATA ELEMENT G, LDG (RLIN) | |
| a | .b12477539 |
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| 902 ## - LOCAL DATA ELEMENT B, LDB (RLIN) | |
| a | 260310 |
| 998 ## - LOCAL CONTROL INFORMATION (RLIN) | |
| Operator's initials, OID (RLIN) | 0 |
| Cataloger's initials, CIN (RLIN) | 260309 |
| First date, FD (RLIN) | m |
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| 945 ## - LOCAL PROCESSING INFORMATION (OCLC) | |
| l | mnarc |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) | |
| Koha item type | 67-Electronic Resource |
| 909 ## - LOCAL ITEMS USED | |
| Barcode | Barcode : - |
| CREATED | CREATED : 2026-02-19 |
| RECORD Id | RECORD # : i13574474 |
| LPATRON | LPATRON : 0 |
| LCHKIN | LCHKIN : - |
| RENEWALS | # RENEWALS : 0 |
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| -- | TOT CHKOUT : 0 |
| -- | TOT RENEW : 0 |
| Withdrawn status | Lost status | Damaged status | Not for loan | Home library | Current library | Shelving location | Date acquired | Total checkouts | Full call number | Date last seen | Copy number | Price effective from | Koha item type |
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| Available for Loans | Asian Institute of Technology Library | Asian Institute of Technology Library | Archives | 17/08/2026 | AIT Thesis no.DSAI-25-04 | 17/08/2026 | 1 | 17/08/2026 | 67-Electronic Resource |