Sensitivity Analysis of a BERT-based scholarly recommendation system
With the exponential growth of publicly available datasets, a scholarly recommendation system of datasets would be an essential tool in the field of information filtering. Recommending datasets to users can be formulated as a classification problem where deep learning models can be carefully trained...
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| Format: | Article |
| Language: | English |
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LibraryPress@UF
2022-05-01
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| Series: | Proceedings of the International Florida Artificial Intelligence Research Society Conference |
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| Online Access: | https://journals.flvc.org/FLAIRS/article/view/130595 |
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| _version_ | 1849763277148520448 |
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| author | Jie Zhu Hulin Wu Ashraf Yaseen |
| author_facet | Jie Zhu Hulin Wu Ashraf Yaseen |
| author_sort | Jie Zhu |
| collection | DOAJ |
| description | With the exponential growth of publicly available datasets, a scholarly recommendation system of datasets would be an essential tool in the field of information filtering. Recommending datasets to users can be formulated as a classification problem where deep learning models can be carefully trained. In such a case, when preparing training data for the learning models, one needs to consider different ratios of false and true pairs. Therefore, a sensitivity analysis is necessary. In this work, we conduct a sensitivity analysis using different class ratios on a deep learning model (BERT) for recommending datasets. We found out that our BERT-based recommender model is relatively robust using recommender metrics such as Mean Reciprocal Rank (MRR)@k, Recall@k, etc., except for the extreme class imbalance case (1:5000). Therefore, we conclude that a moderate ratio of the random negative sampling scheme, (in our case 1:10) is reasonable, sufficient and time-efficient in the recommendation system training |
| format | Article |
| id | doaj-art-deb6cfff35644db8a06fe757af9df239 |
| institution | DOAJ |
| issn | 2334-0754 2334-0762 |
| language | English |
| publishDate | 2022-05-01 |
| publisher | LibraryPress@UF |
| record_format | Article |
| series | Proceedings of the International Florida Artificial Intelligence Research Society Conference |
| spelling | doaj-art-deb6cfff35644db8a06fe757af9df2392025-08-20T03:05:26ZengLibraryPress@UFProceedings of the International Florida Artificial Intelligence Research Society Conference2334-07542334-07622022-05-013510.32473/flairs.v35i.13059566794Sensitivity Analysis of a BERT-based scholarly recommendation systemJie Zhu0Hulin Wu1Ashraf Yaseen2The University of Texas Health Science Center at HoustonThe University of Texas Health Science Center at HoustonThe University of Texas Health Science Center at HoustonWith the exponential growth of publicly available datasets, a scholarly recommendation system of datasets would be an essential tool in the field of information filtering. Recommending datasets to users can be formulated as a classification problem where deep learning models can be carefully trained. In such a case, when preparing training data for the learning models, one needs to consider different ratios of false and true pairs. Therefore, a sensitivity analysis is necessary. In this work, we conduct a sensitivity analysis using different class ratios on a deep learning model (BERT) for recommending datasets. We found out that our BERT-based recommender model is relatively robust using recommender metrics such as Mean Reciprocal Rank (MRR)@k, Recall@k, etc., except for the extreme class imbalance case (1:5000). Therefore, we conclude that a moderate ratio of the random negative sampling scheme, (in our case 1:10) is reasonable, sufficient and time-efficient in the recommendation system traininghttps://journals.flvc.org/FLAIRS/article/view/130595recommender systembertsensitivity analysis |
| spellingShingle | Jie Zhu Hulin Wu Ashraf Yaseen Sensitivity Analysis of a BERT-based scholarly recommendation system Proceedings of the International Florida Artificial Intelligence Research Society Conference recommender system bert sensitivity analysis |
| title | Sensitivity Analysis of a BERT-based scholarly recommendation system |
| title_full | Sensitivity Analysis of a BERT-based scholarly recommendation system |
| title_fullStr | Sensitivity Analysis of a BERT-based scholarly recommendation system |
| title_full_unstemmed | Sensitivity Analysis of a BERT-based scholarly recommendation system |
| title_short | Sensitivity Analysis of a BERT-based scholarly recommendation system |
| title_sort | sensitivity analysis of a bert based scholarly recommendation system |
| topic | recommender system bert sensitivity analysis |
| url | https://journals.flvc.org/FLAIRS/article/view/130595 |
| work_keys_str_mv | AT jiezhu sensitivityanalysisofabertbasedscholarlyrecommendationsystem AT hulinwu sensitivityanalysisofabertbasedscholarlyrecommendationsystem AT ashrafyaseen sensitivityanalysisofabertbasedscholarlyrecommendationsystem |