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 establis...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Graz University of Technology
2021-02-01
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| Series: | Journal of Universal Computer Science |
| Subjects: | |
| Online Access: | https://lib.jucs.org/article/65096/download/pdf/ |
| _version_ | 1831775411638370304 |
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| author | Bilal Abu-Salih Hamad Alsawalqah Basima Elshqeirat Tomayess Issa Pornpit Wongthongtham Khadija Khalid Premi |
| author_facet | Bilal Abu-Salih Hamad Alsawalqah Basima Elshqeirat Tomayess Issa Pornpit Wongthongtham Khadija Khalid Premi |
| author_sort | Bilal Abu-Salih |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-12-22T09:13:38Z |
| format | Article |
| id | doaj.art-e9ba2b00205d4f57b1f7dab61906f76a |
| institution | Directory Open Access Journal |
| issn | 0948-6968 |
| language | English |
| last_indexed | 2024-12-22T09:13:38Z |
| publishDate | 2021-02-01 |
| publisher | Graz University of Technology |
| record_format | Article |
| series | Journal of Universal Computer Science |
| spelling | doaj.art-e9ba2b00205d4f57b1f7dab61906f76a2022-12-21T18:31:22ZengGraz University of TechnologyJournal of Universal Computer Science0948-69682021-02-0127220822910.3897/jucs.6509665096Toward a Knowledge-based Personalised Recommender System for Mobile App DevelopmentBilal Abu-Salih0Hamad Alsawalqah1Basima Elshqeirat2Tomayess Issa3Pornpit Wongthongtham4Khadija Khalid Premi5The University of JordanThe University of JordanThe University of JordanCurtin UniversityThe University of Western AustraliaUniversité de ParisOver 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.https://lib.jucs.org/article/65096/download/pdf/Mobile App DevelopmentSoftware EngineeringReco |
| spellingShingle | Bilal Abu-Salih Hamad Alsawalqah Basima Elshqeirat Tomayess Issa Pornpit Wongthongtham Khadija Khalid Premi Toward a Knowledge-based Personalised Recommender System for Mobile App Development Journal of Universal Computer Science Mobile App Development Software Engineering Reco |
| title | Toward a Knowledge-based Personalised Recommender System for Mobile App Development |
| title_full | Toward a Knowledge-based Personalised Recommender System for Mobile App Development |
| title_fullStr | Toward a Knowledge-based Personalised Recommender System for Mobile App Development |
| title_full_unstemmed | Toward a Knowledge-based Personalised Recommender System for Mobile App Development |
| title_short | Toward a Knowledge-based Personalised Recommender System for Mobile App Development |
| title_sort | toward a knowledge based personalised recommender system for mobile app development |
| topic | Mobile App Development Software Engineering Reco |
| url | https://lib.jucs.org/article/65096/download/pdf/ |
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