1. 一种基于多任务学习的科学文献推荐算法.
- Author
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白莹琦 and 帕丽旦·吐尔逊
- Subjects
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SCIENTIFIC literature , *SCIENTIFIC method , *WORD order (Grammar) , *SEMANTICS , *DEEP learning , *RECOMMENDER systems - Abstract
Traditional recommendation algorithms map text content through topic model or mean value of word vectorization. For the issue that existing methods cannot make full use of text information or ignore word order information, this study proposes a multi-task learning recommendation method for scientific literature. Based on the multi-task learning framework, an encoder is designed and a GL model is established. The GL model is trained to combine content recommendation and text metadata prediction, which improves the sparsity of traditional collaborative filtering and regularizes the collaborative filtering model. Finally, an evaluation test is carried out on public and private data sets respectively, and the superiority of the proposed method is demonstrated by comparing with the existing classical methods. [ABSTRACT FROM AUTHOR]
- Published
- 2023
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