Topic based document modeling for information filtering
Information Filtering (IF), which has been popularly studied in recent years, is one of the areas that applies document retrieval techniques for dealing with the huge amount of information. In IF systems, modelling user’s interest and filtering relevant documents are major parts of the systems. Var...
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| Format: | Article |
| Language: | English |
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Can Tho University Publisher
2023-10-01
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| Series: | CTU Journal of Innovation and Sustainable Development |
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| Online Access: | http://web2010.thanhtoan/index.php/ctujs/article/view/715 |
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| _version_ | 1850235688078802944 |
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| author | Tran Diem Hanh Nguyen |
| author_facet | Tran Diem Hanh Nguyen |
| author_sort | Tran Diem Hanh Nguyen |
| collection | DOAJ |
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Information Filtering (IF), which has been popularly studied in recent years, is one of the areas that applies document retrieval techniques for dealing with the huge amount of information. In IF systems, modelling user’s interest and filtering relevant documents are major parts of the systems. Various approaches have been proposed for modelling the first component. In this study, we utilized a topic-modelling technique, Latent Dirichlet Topic Modelling, to model user’s interest for IFs. In particular, an extended model of it to represent user’s interest named Latent Dirichlet Topic Modelling with high Frequency Occurrences, shorted as LDA_HF, was proposed with the intention to enhance retrieving performance of IFs. The new model was then compared to the existing methods in modelling user’s interest such as BM25, pLSA, and LDA_IF over the big benchmark datasets, RCV1 and R8. The results of extensive experiments showed that the new proposed model outperformed all the state-of-the-art baseline models in user modelling such as BM25, pLSA and LDA_IF according to 4 major measurement metrics including Top20, B/P, MAP, and F1. Hence, the model LDA_HF promises one of the reliable methods of enhancing performance of IFs.
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| format | Article |
| id | doaj-art-a40f7f4df40f4e28a99253d25e874bb5 |
| institution | OA Journals |
| issn | 2588-1418 2815-6412 |
| language | English |
| publishDate | 2023-10-01 |
| publisher | Can Tho University Publisher |
| record_format | Article |
| series | CTU Journal of Innovation and Sustainable Development |
| spelling | doaj-art-a40f7f4df40f4e28a99253d25e874bb52025-08-20T02:02:10ZengCan Tho University PublisherCTU Journal of Innovation and Sustainable Development2588-14182815-64122023-10-0115Special issue: ISDSTopic based document modeling for information filteringTran Diem Hanh Nguyen0a:1:{s:5:"en_US";s:19:"Tra Vinh University";} Information Filtering (IF), which has been popularly studied in recent years, is one of the areas that applies document retrieval techniques for dealing with the huge amount of information. In IF systems, modelling user’s interest and filtering relevant documents are major parts of the systems. Various approaches have been proposed for modelling the first component. In this study, we utilized a topic-modelling technique, Latent Dirichlet Topic Modelling, to model user’s interest for IFs. In particular, an extended model of it to represent user’s interest named Latent Dirichlet Topic Modelling with high Frequency Occurrences, shorted as LDA_HF, was proposed with the intention to enhance retrieving performance of IFs. The new model was then compared to the existing methods in modelling user’s interest such as BM25, pLSA, and LDA_IF over the big benchmark datasets, RCV1 and R8. The results of extensive experiments showed that the new proposed model outperformed all the state-of-the-art baseline models in user modelling such as BM25, pLSA and LDA_IF according to 4 major measurement metrics including Top20, B/P, MAP, and F1. Hence, the model LDA_HF promises one of the reliable methods of enhancing performance of IFs. http://web2010.thanhtoan/index.php/ctujs/article/view/715Information filtering, information retrieval, topic models, topic modelling |
| spellingShingle | Tran Diem Hanh Nguyen Topic based document modeling for information filtering CTU Journal of Innovation and Sustainable Development Information filtering, information retrieval, topic models, topic modelling |
| title | Topic based document modeling for information filtering |
| title_full | Topic based document modeling for information filtering |
| title_fullStr | Topic based document modeling for information filtering |
| title_full_unstemmed | Topic based document modeling for information filtering |
| title_short | Topic based document modeling for information filtering |
| title_sort | topic based document modeling for information filtering |
| topic | Information filtering, information retrieval, topic models, topic modelling |
| url | http://web2010.thanhtoan/index.php/ctujs/article/view/715 |
| work_keys_str_mv | AT trandiemhanhnguyen topicbaseddocumentmodelingforinformationfiltering |