Publicação
Applying LLM-based entity matching for hierarchical product categorization in e-commerce
| datacite.subject.fos | Ciências Sociais::Economia e Gestão | pt_PT |
| dc.contributor.advisor | Han, Qiwei | |
| dc.contributor.author | Markwardt, Elias | |
| dc.date.accessioned | 2025-03-27T15:14:35Z | |
| dc.date.embargo | 2028-01-22 | |
| dc.date.issued | 2025-01-22 | |
| dc.date.submitted | 2025-01-22 | |
| dc.description.abstract | This research explored techniques to improve Large Language Models performance for Hi erarchical Product Classification (HPC), including optimized fine-tuning, optimal prompting techniques, taxonomy-specific Knowledge Graphs, leveraging Retrieval-Augmented Genera tion, and implementing LLM-based Entity Matching. Tested on benchmark datasets Icecat and WDC-222, these methods significantly enhanced LLMs’ ability to solve HPC tasks across var ious scenarios. Results achieved a hierarchical F1-score (hF) of 0.921, surpassing traditional DL benchmarks (0.85 hF). While not outperforming proprietary models like GPT, the proposed approaches offer a cost-efficient and effective alternative for businesses, demonstrating strong performance without reliance on expensive LLM solutions. | pt_PT |
| dc.identifier.tid | 203927737 | pt_PT |
| dc.identifier.uri | http://hdl.handle.net/10362/181475 | |
| dc.language.iso | eng | pt_PT |
| dc.relation | UID/ECO/00124/2013 | pt_PT |
| dc.subject | Large Language Models | pt_PT |
| dc.subject | Hierarchical classification | pt_PT |
| dc.subject | E-Commerce | pt_PT |
| dc.subject | In-Context Learning | pt_PT |
| dc.subject | Fine tuning | pt_PT |
| dc.subject | Prompt Engineering | pt_PT |
| dc.subject | Knowledge graphs | pt_PT |
| dc.subject | Retrieval Augmented Generation | pt_PT |
| dc.subject | Entity matching | pt_PT |
| dc.title | Applying LLM-based entity matching for hierarchical product categorization in e-commerce | pt_PT |
| dc.type | master thesis | |
| dspace.entity.type | Publication | |
| rcaap.rights | embargoedAccess | pt_PT |
| rcaap.type | masterThesis | pt_PT |
| thesis.degree.name | A Work Project, presented as part of the requirements for the Award of a Master’s degree in Business Analytics from the Nova School of Business and Economics | pt_PT |
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