An Edge Intelligence Empowered Recommender System Enabling Cultural Heritage Applications
- Authors
- Su, Xin; Sperli, Giancarlo; Moscato, Vincenzo; Picariello, Antonio; Esposito, Christian; Choi, Chang
- Issue Date
- Jul-2019
- Publisher
- IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
- Keywords
- Big Data; cultural heritage (CH); edge artificial intelligence (AI); recommender system
- Citation
- IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS, v.15, no.7, pp.4266 - 4275
- Journal Title
- IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS
- Volume
- 15
- Number
- 7
- Start Page
- 4266
- End Page
- 4275
- URI
- https://scholarworks.bwise.kr/gachon/handle/2020.sw.gachon/78562
- DOI
- 10.1109/TII.2019.2908056
- ISSN
- 1551-3203
- Abstract
- Recommender systems are increasingly playing an important role in our life, enabling users to find "what they need" within large data collections and supporting a variety of applications, from e-commerce to e-tourism. In this paper, we present a Big Data architecture supporting typical cultural heritage applications. On the top of querying, browsing, and analyzing cultural contents coming from distributed and heterogeneous repositories, we propose a novel user-centered recommendation strategy for cultural items suggestion. Despite centralizing the processing operations within the cloud, the vision of edge intelligence has been exploited by having a mobile app (Smart Search Museum) to perform semantic searches andmachine-learningbased inference so as to be capable of suggesting museums, together with other items of interest, to users when they are visiting a city, exploiting jointly recommendation techniques and edge artificial intelligence facilities. Experimental results on accuracy and user satisfaction show the goodness of the proposed application.
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Collections - IT융합대학 > 컴퓨터공학과 > 1. Journal Articles
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