Semantic Trajectory Analytics and Recommender Systems in Cultural Spaces

Sotiris Angelis,Konstantinos Kotis,Dimitris Spiliotopoulos

doi:10.3390/bdcc5040080

Sotiris Angelis, Konstantinos Kotis + Show 1 more

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https://doi.org/10.3390/bdcc5040080

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Abstract

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.

Highlights

Visitors of cultural spaces are usually offered a rather static and less personalised experience, e.g., a group-organised guided tour of exhibits in museum rooms
Cai et al [42] present an itinerary recommender system (RS) based on semantic trajectory pattern mining from geo-tagged photos in order to provide a suggestion of points of interest (POI)
Semantic itineraries are built by annotating raw trajectories with application-dependent contextual and spatial semantics extracted from geo-tagged photos and environmental data like day, type, time, weather conditions, and place names

Summary

Introduction

Visitors of cultural spaces (e.g., museums, archaeological sites) are usually offered a rather static and less personalised experience, e.g., a group-organised guided tour of exhibits in museum rooms To overcome this problem, there have been numerous studies that utilise advancements in recent technologies, such as IoT and pervasive computing technologies, to monitor and analyse visitor movement and interactions within cultural spaces [1,2]. Analysed data (with high volume, velocity, and variety) gathered from sensors (streaming/dynamic data) and datastores/databases (historical/static data) are used to recognise/infer visitor preferences and personal interests to propose and eventually deliver an enhanced cultural experience This is achieved either by providing personalised and enriched content or by suggesting personalised navigation in the cultural space. The existing infrastructure enables applications to produce a vast amount of streaming data that include information about locations and places that users are visiting and the paths/routes/trajectories the users are following, as an aggregation of connected spatial points in specific time-lapses [4,5,6,7]

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