Building a hybrid recommendation engine on big data technologies

In this thesis, development of recommendation system for GalePress, which is a digital publishing platform, has been discussed. Within the scope of the thesis study, the study areas and histories related to the recommendation systems were examined. In addition, architectures that built on big data technologies were examined, related technologies and type of recommendation systems in the literature were reviewed. After the literature review, the architecture and the technologies to be used for GalePress's recommendation system had been revealed. Subsequently the technologies and architecture to be used had been determined, the data had been transferred to the platform, pre-analyzed and then pre-processed. The model had been trained and tested with the obtained data. Finally, the system has been completed with the hybridization application.