• RecomMetz: A context-aware kno...

Result: RecomMetz: A context-aware knowledge-based mobile recommender system for movie showtimes

Title:
RecomMetz: A context-aware knowledge-based mobile recommender system for movie showtimes
Authors:
COLOMBO-MENDOZA, Luis Omar, VALENCIA-GARCIA, Rafael, RODRIGUEZ-GONZALEZ, Alejandro, ALOR-HERNANDEZ, Giner, SAMPER-ZAPATER, José Javier
Source:
Expert systems with applications. 42(3):1202-1222
Publisher Information:
Amsterdam: Elsevier, 2015.
Publication Year:
2015
Physical Description:
print, 3/4 p
Original Material:
INIST-CNRS
Subject Terms:
Computer science, Informatique, Sciences exactes et technologie, Exact sciences and technology, Sciences appliquees, Applied sciences, Informatique; automatique theorique; systemes, Computer science; control theory; systems, Logiciel, Software, Systèmes informatiques et systèmes répartis. Interface utilisateur, Computer systems and distributed systems. User interface, Organisation des mémoires. Traitement des données, Memory organisation. Data processing, Systèmes d'information. Bases de données, Information systems. Data bases, Intelligence artificielle, Artificial intelligence, Reconnaissance et synthèse de la parole et du son. Linguistique, Speech and sound recognition and synthesis. Linguistics, A froid, Cold process, En frío, Analyse sémantique, Semantic analysis, Análisis semántico, Base de connaissances, Knowledge base, Base conocimiento, Contexte, Context, Contexto, Efficacité, Efficiency, Eficacia, Foule, Crowd, Multitud, Interface utilisateur, User interface, Interfase usuario, Localisation, Localization, Localización, Loisir, Leisure, Ocio, Modélisation, Modeling, Modelización, Métrique, Metric, Métrico, Ontologie, Ontology, Ontología, Personnalisation, Customization, Personalización, Raisonnement, Reasoning, Razonamiento, Recommandation, Recommendation, Recomendación, Relation sémantique, Semantic relation, Relación semántica, Sensibilité contexte, Context aware, Sensibilidad contexto, Service utilisateur, User service, Servicio usuario, Similitude, Similarity, Similitud, Web sémantique, Semantic web, Web semántica, Informatique mobile, Mobile computing, Informática móvil, Context-aware systems, Knowledge-based recommender systems, Ontology reasoning, Semantic Web
Document Type:
Academic journal Article
File Description:
text
Language:
English
Author Affiliations:
Facultad de Informatica, Universidad de Murcia, Campus de Espinardo, Spain
Department of Engineering, School of Engineering, Universidad Internacional de la Rioja, Spain
Division of Research and Postgraduate Studies, Instituto Tecnológico de Orizaba, Mexico
Departament d'Informàtica. Escola Tècnica Superior d'Enginyeria, Universitat de València, Av. de la Universitat, 46100 Burjassot, València, Spain
ISSN:
0957-4174
Access URL:
http://pascal-francis.inist.fr/vibad/index.php?action=search&terms=28928448
Rights:
Copyright 2015 INIST-CNRS
CC BY 4.0
Sauf mention contraire ci-dessus, le contenu de cette notice bibliographique peut être utilisé dans le cadre d’une licence CC BY 4.0 Inist-CNRS / Unless otherwise stated above, the content of this bibliographic record may be used under a CC BY 4.0 licence by Inist-CNRS / A menos que se haya señalado antes, el contenido de este registro bibliográfico puede ser utilizado al amparo de una licencia CC BY 4.0 Inist-CNRS
Notes:
Computer science; theoretical automation; systems
Accession Number:
edscal.28928448
Database:
PASCAL Archive

Further Information

Recommender systems are used to provide filtered information from a large amount of elements. They provide personalized recommendations on products or services to users. The recommendations are intended to provide interesting elements to users. Recommender systems can be developed using different techniques and algorithms where the selection of these techniques depends on the area in which they will be applied. This paper proposes a recommender system in the leisure domain, specifically in the movie showtimes domain. The system proposed is called RecomMetz, and it is a context-aware mobile recommender system based on Semantic Web technologies. In detail, a domain ontology primarily serving a semantic similarity metric adjusted to the concept of packages of single items was developed in this research. In addition, location, crowd and time were considered as three different kinds of contextual information in RecomMetz. In a nutshell, RecomMetz has unique features: (1) the items to be recommended have a composite structure (movie theater + movie + showtime), (2) the integration of the time and crowd factors into a context-aware model, (3) the implementation of an ontology-based context modeling approach and (4) the development of a multi-platform native mobile user interface intended to leverage the hardware capabilities (sensors) of mobile devices. The evaluation results show the efficiency and effectiveness of the recommendation mechanism implemented by RecomMetz in both a cold-start scenario and a no cold-start scenario.