Knowledge-Enriched Recommendations: Bridging the Gap in Alloy Material Selection With Large Language Models
Navigating materials performance databases efficiently is a persistent challenge in materials science and engineering, particularly in the selection of alloy materials. While recommendation systems address information overload, traditional approaches relying on historical user data face limitations...
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| Format: | Article |
| Language: | English |
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IEEE
2025-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10937740/ |
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| author | Tongwei Wu Shiyu Du Yiming Zhang Honggang Li |
| author_facet | Tongwei Wu Shiyu Du Yiming Zhang Honggang Li |
| author_sort | Tongwei Wu |
| collection | DOAJ |
| description | Navigating materials performance databases efficiently is a persistent challenge in materials science and engineering, particularly in the selection of alloy materials. While recommendation systems address information overload, traditional approaches relying on historical user data face limitations such as data sparsity and cold-start issues. This study presents a novel recommendation model that integrates domain-specific knowledge graphs with large language models (LLMs) to enhance recommendation accuracy in alloy material selection. A knowledge graph for alloys is developed, encapsulating technical material data and relationships to improve retrieval and recommendation outcomes. LLMs are employed for label clustering and natural language-based instruction-following to craft user profiles and enhance data representation. Two graph enhancement strategies, integrated with attention mechanisms, effectively capture user preferences. Experimental results on a ferroalloy dataset demonstrate the model’s superior performance compared to baseline methods, significantly addressing data sparsity while offering personalized, accurate recommendations. This research bridges the gap between knowledge graphs and LLMs in recommendation systems, contributing a flexible, intelligent solution to streamline material selection processes. |
| format | Article |
| id | doaj-art-162b0e5bd38f451694699da3658207c2 |
| institution | DOAJ |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-162b0e5bd38f451694699da3658207c22025-08-20T02:53:37ZengIEEEIEEE Access2169-35362025-01-0113531245313910.1109/ACCESS.2025.355412510937740Knowledge-Enriched Recommendations: Bridging the Gap in Alloy Material Selection With Large Language ModelsTongwei Wu0https://orcid.org/0009-0005-2912-7747Shiyu Du1https://orcid.org/0000-0001-6707-3915Yiming Zhang2https://orcid.org/0000-0003-3857-8433Honggang Li3Faculty of Electrical Engineering and Computer Science, Ningbo University, Ningbo, ChinaZhejiang Key Laboratory of Data-Driven High-Safety Energy Materials and Applications, Ningbo Key Laboratory of Special Energy Materials and Chemistry, Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences, Ningbo, ChinaZhejiang Key Laboratory of Data-Driven High-Safety Energy Materials and Applications, Ningbo Key Laboratory of Special Energy Materials and Chemistry, Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences, Ningbo, ChinaFaculty of Electrical Engineering and Computer Science, Ningbo University, Ningbo, ChinaNavigating materials performance databases efficiently is a persistent challenge in materials science and engineering, particularly in the selection of alloy materials. While recommendation systems address information overload, traditional approaches relying on historical user data face limitations such as data sparsity and cold-start issues. This study presents a novel recommendation model that integrates domain-specific knowledge graphs with large language models (LLMs) to enhance recommendation accuracy in alloy material selection. A knowledge graph for alloys is developed, encapsulating technical material data and relationships to improve retrieval and recommendation outcomes. LLMs are employed for label clustering and natural language-based instruction-following to craft user profiles and enhance data representation. Two graph enhancement strategies, integrated with attention mechanisms, effectively capture user preferences. Experimental results on a ferroalloy dataset demonstrate the model’s superior performance compared to baseline methods, significantly addressing data sparsity while offering personalized, accurate recommendations. This research bridges the gap between knowledge graphs and LLMs in recommendation systems, contributing a flexible, intelligent solution to streamline material selection processes.https://ieeexplore.ieee.org/document/10937740/Recommender systemsknowledge graphlarge language modelsdata augmentation |
| spellingShingle | Tongwei Wu Shiyu Du Yiming Zhang Honggang Li Knowledge-Enriched Recommendations: Bridging the Gap in Alloy Material Selection With Large Language Models IEEE Access Recommender systems knowledge graph large language models data augmentation |
| title | Knowledge-Enriched Recommendations: Bridging the Gap in Alloy Material Selection With Large Language Models |
| title_full | Knowledge-Enriched Recommendations: Bridging the Gap in Alloy Material Selection With Large Language Models |
| title_fullStr | Knowledge-Enriched Recommendations: Bridging the Gap in Alloy Material Selection With Large Language Models |
| title_full_unstemmed | Knowledge-Enriched Recommendations: Bridging the Gap in Alloy Material Selection With Large Language Models |
| title_short | Knowledge-Enriched Recommendations: Bridging the Gap in Alloy Material Selection With Large Language Models |
| title_sort | knowledge enriched recommendations bridging the gap in alloy material selection with large language models |
| topic | Recommender systems knowledge graph large language models data augmentation |
| url | https://ieeexplore.ieee.org/document/10937740/ |
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