Context-based recommendation
With the rapid growth of the scientific literature, citation recommendation systems able to speed up literature review and citing process during a research process. Recent approaches use bag-of-word retrieval to represent the documents, which discards word order information which is important in rep...
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| Format: | Final Year Project (FYP) |
| Language: | English |
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Nanyang Technological University
2021
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| Online Access: | https://hdl.handle.net/10356/150326 |
| _version_ | 1811676502374219776 |
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| author | Lim, Zi Heng |
| author2 | Lihui CHEN |
| author_facet | Lihui CHEN Lim, Zi Heng |
| author_sort | Lim, Zi Heng |
| collection | NTU |
| description | With the rapid growth of the scientific literature, citation recommendation systems able to speed up literature review and citing process during a research process. Recent approaches use bag-of-word retrieval to represent the documents, which discards word order information which is important in representation for documents. This project presents a method of recommend candidate references using document representations based on context of each document by learning document representations that incorporate inter-document document relatedness using citation graph and the state-of-the-art Transformer language model. Documents can be embedded into a high-dimensional vector space. Given a query document, it can be encoded into a vector which its nearest neighbours could be retrieved as candidates for citation. A recommendation web application is implemented to facilitate the citation recommendation. |
| first_indexed | 2024-10-01T02:22:29Z |
| format | Final Year Project (FYP) |
| id | ntu-10356/150326 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2024-10-01T02:22:29Z |
| publishDate | 2021 |
| publisher | Nanyang Technological University |
| record_format | dspace |
| spelling | ntu-10356/1503262023-07-07T18:19:46Z Context-based recommendation Lim, Zi Heng Lihui CHEN School of Electrical and Electronic Engineering ELHCHEN@ntu.edu.sg Engineering::Electrical and electronic engineering Engineering::Computer science and engineering::Information systems::Information storage and retrieval With the rapid growth of the scientific literature, citation recommendation systems able to speed up literature review and citing process during a research process. Recent approaches use bag-of-word retrieval to represent the documents, which discards word order information which is important in representation for documents. This project presents a method of recommend candidate references using document representations based on context of each document by learning document representations that incorporate inter-document document relatedness using citation graph and the state-of-the-art Transformer language model. Documents can be embedded into a high-dimensional vector space. Given a query document, it can be encoded into a vector which its nearest neighbours could be retrieved as candidates for citation. A recommendation web application is implemented to facilitate the citation recommendation. Bachelor of Engineering (Electrical and Electronic Engineering) 2021-06-13T11:47:09Z 2021-06-13T11:47:09Z 2021 Final Year Project (FYP) Lim, Z. H. (2021). Context-based recommendation. Final Year Project (FYP), Nanyang Technological University, Singapore. https://hdl.handle.net/10356/150326 https://hdl.handle.net/10356/150326 en A3043-201 application/pdf Nanyang Technological University |
| spellingShingle | Engineering::Electrical and electronic engineering Engineering::Computer science and engineering::Information systems::Information storage and retrieval Lim, Zi Heng Context-based recommendation |
| title | Context-based recommendation |
| title_full | Context-based recommendation |
| title_fullStr | Context-based recommendation |
| title_full_unstemmed | Context-based recommendation |
| title_short | Context-based recommendation |
| title_sort | context based recommendation |
| topic | Engineering::Electrical and electronic engineering Engineering::Computer science and engineering::Information systems::Information storage and retrieval |
| url | https://hdl.handle.net/10356/150326 |
| work_keys_str_mv | AT limziheng contextbasedrecommendation |