QSMatching vs Vector model: comparing effectiveness in questionnaires retrieval
Article No.: 26, Pages 1 - 8
Abstract
Elaborating an useful questionnaire represents an important task for descriptive research. Poorly elaborated questions can lead to answers with meaningless brased or naive interpretations. Therefore, it may be interesting to reuse, partially or totally, questionnaires already created with the same purpose. In this paper we compare QSMatching with the vector model to calculate the similarity between questionnaires and consequently to obtain a ranking of questionnaires according to the user's query. In order to verify the effectiveness, an experiment was carried out comparing QSMatching and the vector model. The result of the analysis of the experiment shows that QSMatching is more effective than the vector model for questionnaires retrieval.
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June 2018
578 pages
ISBN:9781450365598
DOI:10.1145/3229345
Copyright © 2018 ACM.
© 2018 Association for Computing Machinery. ACM acknowledges that this contribution was authored or co-authored by an employee, contractor or affiliate of a national government. As such, the Government retains a nonexclusive, royalty-free right to publish or reproduce this article, or to allow others to do so, for Government purposes only.
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- SIGAPP: ACM Special Interest Group on Applied Computing
- SIGMIS: ACM Special Interest Group on Management Information Systems
- SBC: Sociedade Brasileira de Computação
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Association for Computing Machinery
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Published: 04 June 2018
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