DATAtourist: A Constraint-Based Recommender System Using DATAtourisme Ontology

Boudjemaa Boudaa, Djamila Figuir, Slimane Hammoudi and Sidi mohamed Benslimane
Additional contact information
Boudjemaa Boudaa: University of Tiaret, Tiaret, Algeria
Djamila Figuir: University of Tiaret, Tiaret, Algeria
Slimane Hammoudi: ESEO, Angers, France
Sidi mohamed Benslimane: LabRI Laboratory, Ecole Superieure en Informatique, Sidi Bel-Abbes, Algeria

International Journal of Decision Support System Technology (IJDSST), 2021, vol. 13, issue 2, 1-23

Abstract: Collaborative and content-based recommender systems are widely employed in several activity domains helping users in finding relevant products and services (i.e., items). However, with the increasing features of items, the users are getting more demanding in their requirements, and these recommender systems are becoming not able to be efficient for this purpose. Built on knowledge bases about users and items, constraint-based recommender systems (CBRSs) come to meet the complex user requirements. Nevertheless, this kind of recommender systems witnesses a rarity in research and remains underutilised, essentially due to difficulties in knowledge acquisition and/or in their software engineering. This paper details a generic software architecture for the CBRSs development. Accordingly, a prototype mobile application called DATAtourist has been realized using DATAtourisme ontology as a recent real-world knowledge source in tourism. The DATAtourist evaluation under varied usage scenarios has demonstrated its usability and reliability to recommend personalized touristic points of interest.

Date: 2021
References: View complete reference list from CitEc
Citations:

Downloads: (external link)
http://services.igi-global.com/resolvedoi/resolve. ... 18/IJDSST.2021040104 (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:igg:jdsst0:v:13:y:2021:i:2:p:1-23

Access Statistics for this article

International Journal of Decision Support System Technology (IJDSST) is currently edited by Shaofeng Liu

More articles in International Journal of Decision Support System Technology (IJDSST) from IGI Global
Bibliographic data for series maintained by Journal Editor ().