Personalized Micro-Service Recommendation System for Online News

Asenova, Marchela; Chrysoulas, Christos

Abstract

In the era of artificial intelligence and high technology advance our life is dependent on them in every aspect. The dynamic environment forces us to plan our time with conscious and every minute is valuable. To help individuals and corporations see information that is only relevant to them, recommendation systems are in place. Popular platforms that such as Amazon, Ebay, Netflix, YouTube, make use of advanced recommendation systems to better serve the needed of their users. This research paper gives insight of building a microservice recommendation system for online news. Research in recommendation systems is mainly focused on improving user’s experience based mainly on personalization information, such as preferences, and searching history. To determine the initial preferences of a user an initial menu of topics/themes is provided for the user to choose from. In order to reflect as precise as possible the searching interests regarding news of user, all of his interactions are thoroughly recorded and in depth analyzed, based on advanced machine learning techniques, when adjusting the news topics, the user is interested for. Based on the aforementioned approach, a personalized recommendation system for online news has been developed. Existing techniques has been researched and evaluated to aid the decision about picking the best approach for the software to be implemented. Frameworks/technologies used for the development are Java 8, Spring boot, Spring MVC, Maven and MongoDB.

• Presentation Conference Type: Conference Paper (published)
• Conference Name: The 6th International Symposium on Emerging Information, Communication and Networks (EICN 2019)
• Acceptance Date: Jun 30, 2019
• Online Publication Date: Nov 21, 2019
• Publication Date: 2019
• Deposit Date: Feb 10, 2020
• Publicly Available Date: Feb 11, 2020
• Journal: Procedia Computer Science
• Print ISSN: 1877-0509
• Publisher: Elsevier
• Peer Reviewed: Peer Reviewed
• Volume: 160
• Pages: 610-615
• DOI: https://doi.org/10.1016/j.procs.2019.11.039
• Public URL: http://researchrepository.napier.ac.uk/Output/2548297