Authors: Messaoud Chaa; Omar Nouali; Patrice Bellot
Addresses: Department of Computer Science, Faculty of Exact Sciences, University of Bejaia, 06000 Bejaia, Algeria; Research Center on Scientific and Technical Information, CERIST, Ben Aknoun 16030, Algiers, Algeria ' Research Center on Scientific and Technical Information, CERIST, Ben Aknoun 16030, Algiers, Algeria ' Aix Marseille University, Université de Toulon, CNRS, LIS, Campus de Saint Jérôme, Marseille, France
Abstract: The continued increase in the use of smartphones and other mobile devices has led to a substantial increase in the demand for mobile applications. With the growing availability of mobile apps, retrieving the right application from a large set has become difficult. However, the existing term-based search engines tend to retrieve relevant apps based on query terms rather than considering app features really required by users, such as functionalities, technical or user-interface characteristics. The novelty of this paper lies in extracting app features from app description and social users' reviews, extracting user-requested features and matching between them to get the feature-based score. In addition, we propose effective techniques that extract and weight features requested in the query. Finally, we combine feature-based and term-based scores together to obtain the app relevance score. The experimental results indicate that the proposed approach is effective and outperforms the state-of-the-art retrieval models for app retrieval.
Keywords: app retrieval; feature extraction; social information retrieval; natural language processing; NLP; feature-based score; term-based score.
International Journal of Intelligent Information and Database Systems, 2021 Vol.14 No.2, pp.177 - 197
Received: 06 Feb 2020
Accepted: 20 Aug 2020
Published online: 02 Mar 2021 *