Dual-interactive fusion for code-mixed deep representation learning in tag recommendation
Article
Li, Lin, Wang, Peipei, Zheng, Xinhao, Xie, Qing, Tao, Xiaohui and Velasquez, Juan D.. 2023. "Dual-interactive fusion for code-mixed deep representation learning in tag recommendation." Information Fusion. 99. https://doi.org/10.1016/j.inffus.2023.101862
Article Title | Dual-interactive fusion for code-mixed deep representation learning in tag recommendation |
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ERA Journal ID | 20983 |
Article Category | Article |
Authors | Li, Lin, Wang, Peipei, Zheng, Xinhao, Xie, Qing, Tao, Xiaohui and Velasquez, Juan D. |
Journal Title | Information Fusion |
Journal Citation | 99 |
Article Number | 101862 |
Number of Pages | 12 |
Year | 2023 |
Publisher | Elsevier |
ISSN | 1566-2535 |
1872-6305 | |
Digital Object Identifier (DOI) | https://doi.org/10.1016/j.inffus.2023.101862 |
Web Address (URL) | https://www.sciencedirect.com/science/article/pii/S1566253523001781 |
Abstract | Automatic tagging on software information sites is a tag recommendation service. It aims to recommend content-based tags for a software object to help developers make distinctions among software objects. Due to deep correlations between software objects and tags, it is challenging to simultaneously consider the code snippet and text description of a software object. Towards automatic tagging service, we propose a novel CDR4Tag method, Code-mixed Deep Representation learning via dual-interactive fusion for Tag recommendation on software information sites. In CDR4Tag, a code-mixed dual interaction strategy is designed to fuse the deep semantic correlations between software objects and tags into a joint representation space. On the basis of it, the matching probability is predicted to complete our tag recommendation. Comprehensive experimental results on four software information site datasets have demonstrated the effectiveness of our proposed CDR4Tag in tag recommendation compared with the state-of-the-art methods. |
Keywords | Automatic tagging; Interactive information fusion; Code snippet |
ANZSRC Field of Research 2020 | 461103. Deep learning |
Public Notes | Files associated with this item cannot be displayed due to copyright restrictions. |
Byline Affiliations | Wuhan University of Technology, China |
School of Mathematics, Physics and Computing | |
Instituto Sistemas Complejos de Ingeniería (ISCI), Chile | |
University of Chile, Chile |
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https://research.usq.edu.au/item/z262x/dual-interactive-fusion-for-code-mixed-deep-representation-learning-in-tag-recommendation
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