CQAs: Community Question Answering System Using Ensemble Learning Techniques for Job Assistantship

CQAs: Community Question Answering System Using Ensemble Learning Techniques for Job Assistantship
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© 2022 by IJETT Journal
Volume-70 Issue-3
Year of Publication : 2022
Authors : Venkateswara Rao P, A.P Siva kumar
https://doi.org/10.14445/22315381/IJETT-V70I3P214

How to Cite?

Venkateswara Rao P, A.P Siva kumar, "CQAs: Community Question Answering System Using Ensemble Learning Techniques for Job Assistantship," International Journal of Engineering Trends and Technology, vol. 70, no. 3, pp. 126-131, 2022. Crossref, https://doi.org/10.14445/22315381/IJETT-V70I3P214

Abstract
Individuals can search for and post queries or answers through community question and answer systems, which give a forum for exchanging information. It provides a set of responses with links to related queries for a freshly published query or query search system, which might be a long-term process to discover a meaningful answer. To address this, the system proposes a method for classifying the most appropriate and best answers from the archives using comparable queries. The model results reveal that Quora users and congested stacks are additional prospective to just discuss technology when compared to earlier open studies. It may fail if the query`s keywords don`t match the text content of huge papers containing pertinent inquiries about prevailing methods. Additionally, consumers are frequently not specialists and give confusing queries (Q&A) that produce mixed results and expose a flaw in current systems. To overcome these difficulties, researchers are rearranging the primary outcomes and proposing complex ensemble learning methodologies based on emerging technologies and platforms, as well as the amount of time and space required to develop the model.

Keywords
Stackoverflow, Quora, ML, Technologies, Social Media.

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