Semantic Trajectory Analytics and Recommender Systems in Cultural Spaces
Semantic trajectory analytics and personalised recommender systems that enhance user experience are modern research topics that are increasingly getting attention. Semantic trajectories can efficiently model human movement for further analysis and pattern recognition, while personalised recommender...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-12-01
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| Series: | Big Data and Cognitive Computing |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2504-2289/5/4/80 |
| _version_ | 1797506615901945856 |
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| author | Sotiris Angelis Konstantinos Kotis Dimitris Spiliotopoulos |
| author_facet | Sotiris Angelis Konstantinos Kotis Dimitris Spiliotopoulos |
| author_sort | Sotiris Angelis |
| collection | DOAJ |
| description | Semantic trajectory analytics and personalised recommender systems that enhance user experience are modern research topics that are increasingly getting attention. Semantic trajectories can efficiently model human movement for further analysis and pattern recognition, while personalised recommender systems can adapt to constantly changing user needs and provide meaningful and optimised suggestions. This paper focuses on the investigation of open issues and challenges at the intersection of these two topics, emphasising semantic technologies and machine learning techniques. The goal of this paper is twofold: (a) to critically review related work on semantic trajectories and knowledge-based interactive recommender systems, and (b) to propose a high-level framework, by describing its requirements. The paper presents a system architecture design for the recognition of semantic trajectory patterns and for the inferencing of possible synthesis of visitor trajectories in cultural spaces, such as museums, making suggestions for new trajectories that optimise cultural experiences. |
| first_indexed | 2024-03-10T04:35:05Z |
| format | Article |
| id | doaj.art-caa7e45cdcec4143affd2401ba2f177f |
| institution | Directory Open Access Journal |
| issn | 2504-2289 |
| language | English |
| last_indexed | 2024-03-10T04:35:05Z |
| publishDate | 2021-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Big Data and Cognitive Computing |
| spelling | doaj.art-caa7e45cdcec4143affd2401ba2f177f2023-11-23T03:51:25ZengMDPI AGBig Data and Cognitive Computing2504-22892021-12-01548010.3390/bdcc5040080Semantic Trajectory Analytics and Recommender Systems in Cultural SpacesSotiris Angelis0Konstantinos Kotis1Dimitris Spiliotopoulos2Intelligent Systems Lab, Department of Cultural Technology and Communication, University of the Aegean, University Hill, 81100 Mytilene, GreeceIntelligent Systems Lab, Department of Cultural Technology and Communication, University of the Aegean, University Hill, 81100 Mytilene, GreeceDepartment of Management Science and Technology, University of the Peloponnese, 22100 Tripoli, GreeceSemantic trajectory analytics and personalised recommender systems that enhance user experience are modern research topics that are increasingly getting attention. Semantic trajectories can efficiently model human movement for further analysis and pattern recognition, while personalised recommender systems can adapt to constantly changing user needs and provide meaningful and optimised suggestions. This paper focuses on the investigation of open issues and challenges at the intersection of these two topics, emphasising semantic technologies and machine learning techniques. The goal of this paper is twofold: (a) to critically review related work on semantic trajectories and knowledge-based interactive recommender systems, and (b) to propose a high-level framework, by describing its requirements. The paper presents a system architecture design for the recognition of semantic trajectory patterns and for the inferencing of possible synthesis of visitor trajectories in cultural spaces, such as museums, making suggestions for new trajectories that optimise cultural experiences.https://www.mdpi.com/2504-2289/5/4/80semantic trajectoriesrecommender systemsbig data analyticsuser experiencecultural space |
| spellingShingle | Sotiris Angelis Konstantinos Kotis Dimitris Spiliotopoulos Semantic Trajectory Analytics and Recommender Systems in Cultural Spaces Big Data and Cognitive Computing semantic trajectories recommender systems big data analytics user experience cultural space |
| title | Semantic Trajectory Analytics and Recommender Systems in Cultural Spaces |
| title_full | Semantic Trajectory Analytics and Recommender Systems in Cultural Spaces |
| title_fullStr | Semantic Trajectory Analytics and Recommender Systems in Cultural Spaces |
| title_full_unstemmed | Semantic Trajectory Analytics and Recommender Systems in Cultural Spaces |
| title_short | Semantic Trajectory Analytics and Recommender Systems in Cultural Spaces |
| title_sort | semantic trajectory analytics and recommender systems in cultural spaces |
| topic | semantic trajectories recommender systems big data analytics user experience cultural space |
| url | https://www.mdpi.com/2504-2289/5/4/80 |
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