M
Magdalini Eirinaki
Researcher at San Jose State University
Publications - 75
Citations - 3043
Magdalini Eirinaki is an academic researcher from San Jose State University. The author has contributed to research in topics: Recommender system & Personalization. The author has an hindex of 20, co-authored 72 publications receiving 2772 citations. Previous affiliations of Magdalini Eirinaki include Athens University of Economics and Business.
Papers
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Journal ArticleDOI
A building permit system for smart cities: A cloud-based framework
Magdalini Eirinaki,Subhankar Dhar,Shishir Mathur,Adwait Kaley,Arpit Patel,Akshar Joshi,Dhvani Shah +6 more
TL;DR: The proposed smart city permit framework provides a pre-permit processing front-end with permit processing and data analytics & mining modules, and incorporates a data analytics and mining module that enables the continuous improvement of both the end user experience and the permitting and urban planning processes.
Book ChapterDOI
Introducing semantics in web personalization: the role of ontologies
TL;DR: In this article, a semantic web personalization system is proposed, focusing on word sense disambiguation techniques which can be applied in order to semantically annotate the web site's content.
Archiving the Greek Web
TL;DR: A first attempt to archive the Greek Web is presented, addressing the bilingualism issue arising because the content is written in both Greek and English and a combination of IR and content mining techniques is applied in order to semantically characterize the collected content.
Journal ArticleDOI
Blockchain-based recommender systems: Applications, challenges and future opportunities
Yassine Himeur,Yassine Himeur,Aya Sayed,Abdullah Alsalemi,Abdullah Alsalemi,Faycal Bensaali,Abbes Amira,Abbes Amira,Iraklis Varlamis,Magdalini Eirinaki,Christos Sardianos,George Dimitrakopoulos +11 more
TL;DR: In this paper, the authors present a review of blockchain-based recommender systems covering challenges, open issues and solutions, and a well-designed taxonomy is introduced to describe the security and privacy challenges, overview existing frameworks and discuss their applications and benefits when using blockchain.
Journal ArticleDOI
Identification of influential social networkers
TL;DR: This paper introduces ProfileRank, a metric that uses popularity and activity characteristics of each user to rank them in terms of their influence and assesses this algorithm's added value in identifying influential users compared to other commonly used social network analysis metrics.