Preference Aware Service Recommendation Using Collaborative Filtering Approach

Service recommendations are shown as remarkable tools for providing recommendations to users in an appropriate way. In the last few years, the number of customers, online information and services has grown very rapidly, resulting in the big data analysis problem for service recommendation system. Consequently, there is scalability and inefficiency problems associated with the traditional service recommendation system which suffers in processing or analyzing large-scale data. Moreover, most of available service recommendation system gives the same rankings and ratings of services to different users without any considerations of many user’s preferences, and hence it fails to reach user’s personalized requirements. In this paper, we have proposed a Preference-Aware Service Recommendation method, to overcome the above challenges. It aims at recommending the most appropriate and preferred services to the users and provide a personalized service recommendation list in an effective way. Here, users' preferences are captured as keywords, and a user-based Collaborative filtering approach is adopted to create appropriate recommendations. A widely-adopted distributed computing platform, Hadoop is used for the implementation of this approach, which improves its efficiency and scalability in big data environment, using the MapReduce parallel processing method. DOI: 10.17762/ijritcc2321-8169.150510

Implementation of Collaborative Filtering Approach in Preference Aware Service Recommendation

Service recommendations are shown as remarkable tools for providing recommendations to users in an appropriate way. In the last few years, the number of customers, online information and services has grown very rapidly, resulting in the big data analysis problem for service recommendation system. Consequently, there is scalability and inefficiency problems associated with the traditional service recommendation system which suffers in processing or analyzing large-scale data. Moreover, most of available service recommendation system gives the same rankings and ratings of services to different users without any considerations of many user’s preferences, and hence it fails to reach user’s personalized requirements. In this paper, we have proposed a Preference-Aware Service Recommendation method, to overcome the above challenges. It aims at recommending the most appropriate and preferred services to the users and provide a personalized service recommendation list in an effective way. Here, users' preferences are captured as keywords, and a user-based Collaborative filtering approach is adopted to create appropriate recommendations. A widely-adopted distributed computing platform, Hadoop is used for the implementation of this approach, which improves its efficiency and scalability in big data environment, using the MapReduce parallel processing method. DOI: 10.17762/ijritcc2321-8169.15034

Improving Online Education Using Big Data Technologies

In a world in full digital transformation, where new information and communication technologies are constantly evolving, the current challenge of Computing Environments for Human Learning (CEHL) is to search the right way to integrate and harness the power of these technologies. In fact, these environments face many challenges, especially the increased demand for learning, the huge growth in the number of learners, the heterogeneity of available resources as well as the problems related to the complexity of intensive processing and real-time analysis of data produced by e-learning systems, which goes beyond the limits of traditional infrastructures and relational database management systems. This chapter presents a number of solutions dedicated to CEHL around the two big paradigms, namely cloud computing and Big Data. The first part of this work is dedicated to the presentation of an approach to integrate both emerging technologies of the big data ecosystem and on-demand services of the cloud in the e-learning field. It aims to enrich and enhance the quality of e-learning platforms relying on the services provided by the cloud accessible via the internet. It introduces distributed storage and parallel computing of Big Data in order to provide robust solutions to the requirements of intensive processing, predictive analysis, and massive storage of learning data. To do this, a methodology is presented and applied which describes the integration process. In addition, this chapter also addresses the deployment of a distributed e-learning architecture combining several recent tools of the Big Data and based on a strategy of data decentralization and the parallelization of the treatments on a cluster of nodes. Finally, this article aims to develop a Big Data solution for online learning platforms based on LMS Moodle. A course recommendation system has been designed and implemented relying on machine learning techniques, to help the learner select the most relevant learning resources according to their interests through the analysis of learning traces. The realization of this system is done using the learning data collected from the ESTenLigne platform and Spark Framework deployed on Hadoop infrastructure