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

Colombo-Mendoza, Luis Omar; Valencia‐García, Rafael; Rodríguez‐González, Alejandro; Alor-Hernández, Giner; Zapater, José Javier Samper

doi:10.1016/j.eswa.2014.09.016

Cited by 166 publications

(71 citation statements)

References 31 publications

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“…On the other hand, we thought that the approach presented in this work can be integrated, but is not limited to, ontology-based recommenders system applied to domains such as leisure (Colombo-Mendoza et al, 2015) and selling services (Paiva, Costa, & Silva, 2014). This combination can be a medium through which the user experience can be improved through easy-to-use applications.…”

Section: Discussionmentioning

confidence: 92%

ONLI: An ontology-based system for querying DBpedia using natural language paradigm

Paredes-Valverde¹,

Rodríguez-García²,

Ruiz‐Martínez³

et al. 2015

Expert Systems with Applications

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Section: Discussionmentioning

confidence: 92%

ONLI: An ontology-based system for querying DBpedia using natural language paradigm

Paredes-Valverde¹,

Rodríguez-García²,

Ruiz‐Martínez³

et al. 2015

Expert Systems with Applications

Self Cite

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“…Post-filtering (PostF): according to Adomavicius and Tuzhilin (2011), in contextual post-filtering, contextual information is initially ignored, and the ratings are predicted using any traditional recommendation algorithm. Then, the resulting set of recommendations is adjusted (contextualized) for each user using the contextual information (Colombo-Mendoza et al, 2015).…”

Section: Context-awareness Algorithmsmentioning

confidence: 99%

A literature review of recommender systems in the television domain

Véras

Prota

Bispo

et al. 2015

Expert Systems with Applications

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“…In [5] proposed a context aware recommender system called RecomMetz that recommends movie showtimes based on three different kinds of contextual information crowd, location, and time. Items to be recommended in this recommender system have a composite structure (movie_theater + showtime + movie).…”

Section: Introductionmentioning

confidence: 99%

Location based context aware recommender system through user defined rules

Sharma

Kaur

2015

International Conference on Computing, Communication &Amp; Automation

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Recommender systems are a subclass of information filtering system and are widely used in the ecommerce domain [13]. They filter huge amount of data to provide personalized recommendations on services or products to users. Most of the existing approaches to develop a recommender system do not take into account contextual information such as weather, day, time, distance and location to provide recommendations. This paper proposes a location based context aware recommender system [9] that uses a ranking function to provide top-k recommendations to the user. The contextual data is defined by the users in the form of rules and RuleML [1] is chosen as a rule based language. When an active user needs recommendations of nearby places then contextual data in the user-defined RuleML rules is extracted, evaluated and top-k recommendations of nearby places based on the ranking function are presented to the user.

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

Cited by 166 publications

References 31 publications

ONLI: An ontology-based system for querying DBpedia using natural language paradigm

ONLI: An ontology-based system for querying DBpedia using natural language paradigm

A literature review of recommender systems in the television domain

Location based context aware recommender system through user defined rules

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