Leveraging app features to improve mobile app retrieval. (2nd March 2021)

• Record Type: Journal Article
• Authors: Chaa, Messaoud; Nouali, Omar; Bellot, Patrice
• Is Part Of: International journal of intelligent information and database systems. Volume 14:Number 2(2021)
• Page Start: 177
• Page End: 197
• Publication Date: 2021-03-02
• Subjects: app retrieval -- feature extraction -- social information retrieval -- natural language processing -- NLP -- feature-based score -- term-based score; Database management -- Computer programs -- Periodicals; Information retrieval -- Computer programs -- Periodicals; Information storage and retrieval systems -- Computer programs -- Periodicals; Artificial intelligence -- Periodicals; Expert systems (Computer science) -- Periodicals; Intelligent agents (Computer software) -- Periodicals; 006.33
• Languages: English
• ISSNs: 1751-5858

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.