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

TL;DR

This paper introduces a personalized recommender system for mobile app developers that leverages ontology, semantic web, and machine learning to suggest relevant tools, platforms, and components efficiently.

Contribution

It presents a novel framework integrating semantic web, ontology, and machine learning for personalized mobile app development recommendations.

Findings

Enhanced query relevance through semantic query expansion

Effective user profiling with time-aware multidimensional models

Improved recommendation accuracy with integrated filtering techniques

Abstract

Over the last few years, the arena of mobile application development has expanded considerably beyond the balance of the world\'s software markets. With the growing number of mobile software companies, and the mounting sophistication of smartphones\' technology, developers have been building several categories of applications on dissimilar platforms. However, developers confront several challenges through the implementation of mobile application projects. In particular, there is a lack of consolidated systems that are competent to provide developers with personalised services promptly and efficiently. Hence, it is essential to develop tailored systems which 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 fortified set of techniques that are designed to provide mobile app developers with a distinctive platform to browse and search for the personalised artifacts. The proposed system make use of ontology and semantic web technology as well as machine learning techniques. In particular, the new RS framework comprises the following components; (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.