RecomMetz: A context-aware knowledge-based mobile recommender system for movie showtimes. Issue 3 (15th February 2015)

• Record Type: Journal Article
• Authors: Colombo-Mendoza, Luis Omar; Valencia-García, Rafael; Rodríguez-González, Alejandro; Alor-Hernández, Giner; Samper-Zapater, José Javier
• Is Part Of: Expert systems with applications. Volume 42:Issue 3(2015)
• Page Start: 1202
• Page End: 1222
• Publication Date: 2015-02-15
• Subjects: Knowledge-based recommender systems -- Context-aware systems -- Semantic Web -- Ontology reasoning; Expert systems (Computer science) -- Periodicals; Systèmes experts (Informatique) -- Périodiques; Electronic journals; 006.33
• DOI: 10.1016/j.eswa.2014.09.016
• Languages: English
• ISSNs: 0957-4174

Abstract

Highlights: A novel semantic-based recommender system in the leisure domain is proposed. The context-aware approach is based on location, time and crowd information. Recommended items are viewed as composed items: movie theater + movie + showtime. Good performance results were obtained under cold-start real world scenarios. Abstract: 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.