Semantic Trajectory Analytics and Recommender Systems in Cultural Spaces

• Published in: Big data and cognitive computing Vol. 5; no. 4; p. 80
• Main Authors: Angelis, Sotiris, Kotis, Konstantinos, Spiliotopoulos, Dimitris
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.12.2021
• Subjects: Algorithms; Behavior; big data analytics; Computer architecture; cultural space; Customization; Data mining; Human motion; Interactive systems; Knowledge; Machine learning; Museums; Ontology; Pattern analysis; Pattern recognition; Recommender systems; semantic trajectories; Semantic web; Semantics; Trajectory analysis; Trajectory optimization; User experience
• ISSN: 2504-2289; 2504-2289
• DOI: 10.3390/bdcc5040080

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.