Evaluation of a Recommender System for Assisting Novice Game Designers

Machado, Tiago; Gopstein, Daniel; Nov, Oded; Wang, Angela Yee-Moon; Nealen, Andy; Togelius, Julian

doi:10.48550/arxiv.1908.04629

Cited by 1 publication

(1 citation statement)

References 7 publications

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“…Authors or [24] developed a RS utilizing dissimilar knowledge silos to suggest attack patterns based on the use case descriptions in order to alert developers and stakeholders with the possibilities that their software system can get compromised by mimicking the attacker's attitude during the premature stages of software development process. Assisting game developers is also presented in [25] as a RS which incorporates AI to decrease work load, improve self-efficacy, and improve accuracy. Domain modelling as another important area of research, thus authors of [26] support these endeavours by designing a system for automated modelling recommendations.…”

Section: Related Workmentioning

confidence: 99%

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

Abu-Salih¹,

Alsawalqah²,

Elshqeirat³

et al. 2021

jucs

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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.

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Section: Related Workmentioning

confidence: 99%