Journal ArticleDOI
RecomMetz: A context-aware knowledge-based mobile recommender system for movie showtimes
Luis Omar Colombo-Mendoza,Rafael Valencia-García,Alejandro Rodríguez-González,Giner Alor-Hernández,José Javier Samper-Zapater +4 more
TLDR
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.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.read more
Citations
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Journal ArticleDOI
Knowledge-based recommendation: a review of ontology-based recommender systems for e-learning
TL;DR: This study shows that use of ontology for knowledge representation in e-learning recommender systems can improve the quality of recommendations and hybridization of knowledge-based recommendation with other recommendation techniques can enhance the effectiveness of e- learning recommenders.
Journal ArticleDOI
A hybrid knowledge-based recommender system for e-learning based on ontology and sequential pattern mining
TL;DR: The proposed hybrid approach can alleviate both the cold-start and data sparsity problems by making use of ontological domain knowledge and learner’s sequential access pattern respectively before the initial data to work on is available in the recommender system.
Journal ArticleDOI
Characterizing context-aware recommender systems
TL;DR: A framework that characterizes context-aware recommendation processes in terms of the recommendation techniques used at every stage of the process and the techniques used to incorporate context is characterized, providing a clear understanding about the integration of context into recommender systems.
Journal ArticleDOI
Regression-based three-way recommendation
Heng-Ru Zhang,Fan Min,Bing Shi +2 more
TL;DR: Experimental results on the well-known MovieLens data set show that threshold settings are critical to the performance of the recommender, and that two optimal threshold-determination approaches based on the three-way decision model can compute unique optimal thresholds.
Journal ArticleDOI
Context-Aware Recommender System: A Review of Recent Developmental Process and Future Research Direction
Khalid Haruna,Maizatul Akmar Ismail,Suhendroyono Suhendroyono,Damiasih Damiasih,Adi Cilik Pierewan,Haruna Chiroma,Tutut Herawan +6 more
TL;DR: A review of recent developmental processes as a fountainhead for the research of a context-aware recommender system is presented, taking an integrated approach to the complete CARS developmental process, unlike other review papers, which only address a specific aspect of the CARS process.
References
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