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

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.

User Context Ontology for Adaptive Mobile-Phone Interfaces

“…User interface adaptation methodology can be assisted by the knowledge of the context values and respective interaction design. There exist many semantically enriched context aware interaction and information modeling and designing approaches [12]- [15]. The semantic and ontological modeling is intended to provide a shared, formal concepts for as knowledge that can be linked, aligned, reuse and grow through evolution.…”

“…The current state of the art provide many semantic and ontological model developed as lower ontological model for specialized environment and context. The researchers have used the ontological modeling and knowledge representation techniques for the formal modeling of various domain and application concepts for better information retrieval, visualization, analysis and interaction design [12]- [15]. The research under discussion is targeted for a flexible and scalable middle level ontology for an adaptive user interface development.…”

Introduction to Big Data Technology

“…The diverse depictions to the Big data problem have naturally led to provide a plethora of technical perceptions on the Big data paradigm. This has opened doors to several descriptions to Big data in dissimilar contexts toward better understanding to Big data paradigm, its challenges and advantages, and hence, obtaining the true value of it [37][38][39][40][41][42][43]. To differentiate it from the traditional data processing systems, Big data has been amply characterised by the well-known 3Vs (Volume, Velocity and Variety) [44].…”

Semantic Data Discovery from Social Big Data

“…Social media plays a major role in our societies, reshaping the media arena, changing the rules of the game to a large extent, and providing ample space for expression and mutual dialogue. These forces have helped create platforms for the formation of public space or the public domain [4][5][6][7]. The new media have also weakened the role of the so-called gatekeeper and the role of the major traditional media (newspapers, radio, television, news outlets) in prioritising public opinion [8] [9-13].…”