Mobile Bookstore (m-Bookstore)

Mobile technologies and computing are evolving and expanding each day, demanding and creating a much more ubiquitous computing environment. This research project proposes the development and implementation of the Mobile Bookstore - a mobile solution for bookstore businesses. This report presents the final research and study of the development of the Mobile Bookstore as a solution to the problem statements stated in the project proposal as well as in this report, which is considered as the main objective of the study. The Mobile Bookstore will address to the four problem statements, which are the geographical problems, the advancing mobile technologies, ubiquitous demands in computing and large bookstore information requests. These objectives help in answering the question to why this research is done and why would we need a mobile bookstore? With the mobile bookstore, companies can reach out to more customers, anywhere and everywhere using mobile devices. This concept allows for a more ubiquitous business and computing. Major bookstores need to compete and to be on top, implementing the latest technologies to serve its customers, and the mobile technology is one that should be taken advantage of. Browsing the large database of a bookstore can be time-consuming and difficult using expensive kiosks that come in limited numbers. A wireless environment can create wireless networks allowing those with mobile devices to browse through the bookstore database with ease. With this report, the basis for the research of this project will be underlined in detail, including the technologies, means, methods and study of recent researches related to the study. The result of this research project will be the software solution, a system (the Mobile Bookstore), which consists of two modules: the outdoor WAPbased module and the indoor Wireless Network module

The use of machine learning algorithms in recommender systems: A systematic review

The final publication is available at Elsevier via https://doi.org/10.1016/j.eswa.2017.12.020 © 2018. This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0/Recommender systems use algorithms to provide users with product or service recommendations. Recently, these systems have been using machine learning algorithms from the field of artificial intelligence. However, choosing a suitable machine learning algorithm for a recommender system is difficult because of the number of algorithms described in the literature. Researchers and practitioners developing recommender systems are left with little information about the current approaches in algorithm usage. Moreover, the development of recommender systems using machine learning algorithms often faces problems and raises questions that must be resolved. This paper presents a systematic review of the literature that analyzes the use of machine learning algorithms in recommender systems and identifies new research opportunities. The goals of this study are to (i) identify trends in the use or research of machine learning algorithms in recommender systems; (ii) identify open questions in the use or research of machine learning algorithms; and (iii) assist new researchers to position new research activity in this domain appropriately. The results of this study identify existing classes of recommender systems, characterize adopted machine learning approaches, discuss the use of big data technologies, identify types of machine learning algorithms and their application domains, and analyzes both main and alternative performance metrics.Natural Sciences and Engineering Research Council of Canada (NSERC) Ontario Research Fund of the Ontario Ministry of Research, Innovation, and Scienc