Toward a Knowledge-based Personalised Recommender System for Mobile App Development

Over the last few years, the arena of mobile application development has expanded considerably beyond the demand of the world's software markets. With the growing number of mobile software companies and the increasing sophistication of smartphone technology, developers have been establishing se...

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Published in:J.UCS (Annual print and CD-ROM archive ed.) Vol. 27; no. 2; pp. 208 - 229
Main Authors: Abu-Salih, Bilal, Alsawalqah, Hamad, Elshqeirat, Basima, Issa, Tomayess, Wongthongtham, Pornpit, Premi, Khadija Khalid
Format: Journal Article
Language:English
Published: Pensoft Publishers 28.02.2021
Graz University of Technology
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ISSN:0948-695X, 0948-6968
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Abstract Over the last few years, the arena of mobile application development has expanded considerably beyond the demand of the world's software markets. With the growing number of mobile software companies and the increasing sophistication of smartphone technology, developers have been establishing several categories of applications on dissimilar platforms. However, developers confront several challenges when undertaking mobile application projects. In particular, there is a lack of consolidated systems that can competently, promptly and efficiently provide developers with personalised services. Hence, it is essential to develop tailored systems that can recommend appropriate tools, IDEs, platforms, software components and other correlated artifacts to mobile application developers. This paper proposes a new recommender system framework comprising a robust set of techniques that are designed to provide mobile app developers with a specific platform where they can browse and search for personalised artifacts. In particular, the new recommender system framework comprises the following functions: (i) domain knowledge inference module: including various semantic web technologies and lightweight ontologies; (ii) profiling and preferencing: a new proposed time- aware multidimensional user modelling; (iii) query expansion: to improve and enhance the retrieved results by semantically augmenting users' query; and (iv) recommendation and information filtration: to make use of the aforementioned components to provide personalised services to the designated users and to answer a user's query with the minimum mismatches.
AbstractList Over the last few years, the arena of mobile application development has expanded considerably beyond the demand of the world's software markets. With the growing number of mobile software companies and the increasing sophistication of smartphone technology, developers have been establishing several categories of applications on dissimilar platforms. However, developers confront several challenges when undertaking mobile application projects. In particular, there is a lack of consolidated systems that can competently, promptly and efficiently provide developers with personalised services. Hence, it is essential to develop tailored systems that can recommend appropriate tools, IDEs, platforms, software components and other correlated artifacts to mobile application developers. This paper proposes a new recommender system framework comprising a robust set of techniques that are designed to provide mobile app developers with a specific platform where they can browse and search for personalised artifacts. In particular, the new recommender system framework comprises the following functions: (i) domain knowledge inference module: including various semantic web technologies and lightweight ontologies; (ii) profiling and preferencing: a new proposed time- aware multidimensional user modelling; (iii) query expansion: to improve and enhance the retrieved results by semantically augmenting users' query; and (iv) recommendation and information filtration: to make use of the aforementioned components to provide personalised services to the designated users and to answer a user's query with the minimum mismatches.
Over the last few years, the arena of mobile application development has expanded considerably beyond the demand of the world's software markets. With the growing number of mobile software companies and the increasing sophistication of smartphone technology, developers have been establishing several categories of applications on dissimilar platforms. However, developers confront several challenges when undertaking mobile application projects. In particular, there is a lack of consolidated systems that can competently, promptly and efficiently provide developers with personalised services. Hence, it is essential to develop tailored systems that can recommend appropriate tools, IDEs, platforms, software components and other correlated artifacts to mobile application developers. This paper proposes a new recommender system framework comprising a robust set of techniques that are designed to provide mobile app developers with a specific platform where they can browse and search for personalised artifacts. In particular, the new recommender system framework comprises the following functions: (i) domain knowledge inference module: including various semantic web technologies and lightweight ontologies; (ii) profiling and preferencing: a new proposed time- aware multidimensional user modelling; (iii) query expansion: to improve and enhance the retrieved results by semantically augmenting users’ query; and (iv) recommendation and information filtration: to make use of the aforementioned components to provide personalised services to the designated users and to answer a user’s query with the minimum mismatches.
Audience Academic
Author Alsawalqah, Hamad
Wongthongtham, Pornpit
Issa, Tomayess
Abu-Salih, Bilal
Premi, Khadija Khalid
Elshqeirat, Basima
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Snippet Over the last few years, the arena of mobile application development has expanded considerably beyond the demand of the world's software markets. With the...
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SubjectTerms Computer software industry
Mobile App Development
Reco
Software Engineering
Wireless telephone software
Title Toward a Knowledge-based Personalised Recommender System for Mobile App Development
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