Title
A knowledge-based multi-criteria collaborative filtering approach for discovering services in mobile cloud computing platforms
Abstract
In the context of Cloud-based development of mobile applications, third-party services to be integrated by applications often have to be manually selected among many categories and providers at design time. Over the years, recommender systems have proven effective in overcoming the challenges related to the incredible growth of the information on the Web. In an effort to better address this problem, the use of Semantic Web technologies in the development of recommender systems has been gaining momentum in recent years. In this paper, we propose a knowledge-based Collaborative Filtering recommendation approach for the discovery of services in a mobile Cloud computing platform for services-based development. Our approach employs a knowledge-based technique that takes advantage of Semantic Web rule-based reasoning capabilities. A major contribution of this work is a multi-criteria collaborative service evaluation mechanism that is based on a standard service quality framework and is built on top of an ontology-based domain model. A two-part evaluation method that is intended to evaluate the proposed recommendation approach not only from a Computer Science perspective but also from an Information Systems perspective is also presented.
Year
DOI
Venue
2020
10.1007/s10844-018-0527-2
Journal of Intelligent Information Systems
Keywords
Field
DocType
Recommender system, Multi-criteria rating, Collaborative filtering, Semantic web, Knowledge base, Mobile cloud computing
Information system,Data science,Recommender system,Mobile cloud computing,Ontology,Collaborative filtering,Computer science,Semantic Web,Artificial intelligence,Knowledge base,Machine learning,Cloud computing
Journal
Volume
Issue
ISSN
54
1
1573-7675
Citations 
PageRank 
References 
0
0.34
20
Authors
4