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A decision system for computational authors profiling: From machine learning to deep learning.
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- Author(s): Mechti, Seifeddine (AUTHOR); Krichen, Moez (AUTHOR); Ben Noureddine, Dhouha (AUTHOR); Belguith, Lamia H. (AUTHOR)
- Source:
Concurrency & Computation: Practice & Experience. 3/25/2022, Vol. 34 Issue 7, p1-13. 13p. - Source:
- Additional Information
- Subject Terms:
- Abstract: Summary: In this study, we tackle the problem of author profiling. The aim of the proposed approach is to determine the author's age and gender. Once the user connects to the company website, this company collects the available data about him (which is usually very limited). Then, the user receives a service recommendation according to his gender and age. Thus, a context‐specific decision‐making system based on these limited data is required to produce an efficient classification. Such a decision system allows companies to promote their marketing. To obtain the best categorization, machine learning (ML) and deep learning (DL) techniques have been applied in the literature. In this article, we apply both classical ML techniques and recently developed DL techniques. More precisely, we adopt the gated recurrent unit model. Our experiments show that our findings are positively comparable with the best state‐of‐the‐art methods. [ABSTRACT FROM AUTHOR]
- Abstract: Copyright of Concurrency & Computation: Practice & Experience is the property of John Wiley & Sons, Inc. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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