JUCS - Journal of Universal Computer Science 27(2): 208-229, doi: 10.3897/jucs.65096
Toward a Knowledge-based Personalised Recommender System for Mobile App Development
expand article infoBilal Abu-Salih, Hamad Alsawalqah, Basima Elshqeirat, Tomayess Issa§, Pornpit Wongthongtham|, Khadija Khalid Premi
‡ The University of Jordan, Amman, Jordan§ Curtin University, Perth, Australia| The University of Western Australia, Perth, Australia¶ Université de Paris, Paris, France
Open Access

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.

Mobile App Development, Software Engineering, Recommender  Systems, Semantic Analytics, User Profiling, Machine Learning