Recommendation Semantic of Services In Smart City

Benfares, Chaymae; Idrissi, Younès El Bouzekri El; Abouabdellah, Abdellah

doi:10.1145/3090354.3090407

Cited by 8 publications

(5 citation statements)

References 14 publications

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“…The recommendation system suggests multi-types of services and proactively pushes explicit query suggestions to users. Furthermore, Benfares et al (2017) applied semantic of services to provide recommendations in smart cities to address the generation and selection of customized and relevant services to support real-time decision making of users. Additionally, Benfares et al (2016) designed a personalized architecture for patrimony tourism recommendation services in smart city.…”

Section: Prior Recommender Systems In Smart City Domainmentioning

confidence: 99%

A case-based reasoning recommender system for sustainable smart city development

Anthony

2020

AI & Soc

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With the deployment of information and communication technologies (ICTs) and the needs of data and information sharing within cities, smart city aims to provide value-added services to improve citizens’ quality of life. But, currently city planners/developers are faced with inadequate contextual information on the dimensions of smart city required to achieve a sustainable society. Therefore, in achieving sustainable society, there is need for stakeholders to make strategic decisions on how to implement smart city initiatives. Besides, it is required to specify the smart city dimensions to be adopted in making cities smarter for sustainability attainment. But, only a few methods such as big data, internet of things, cloud computing, etc. have been employed to support smart city attainment. Thus, this study integrates case-based reasoning (CBR) as an artificial intelligence technique to develop a recommender system towards promoting smart city planning. CBR provides suggestions on smart city dimensions to be adopted by city planners/decision-makers in making cities smarter and sustainable. Accordingly, survey data were collected from 115 respondents to evaluate the applicability of the implemented CBR recommender system in relation to how the system provides best practice recommendations and retaining of smart city initiatives. Results from descriptive and exploratory factor analyses suggest that the developed system is applicable in supporting smart city adoption. Besides, findings from this study are expected to provide valuable insights for practitioners to develop more practical strategies and for researchers to better understand smart city dimensions.

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Section: Prior Recommender Systems In Smart City Domainmentioning

confidence: 99%

A case-based reasoning recommender system for sustainable smart city development

Anthony

2020

AI & Soc

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“…The ontologies can be written in different programming languages, to provide a formal description of concepts, terms, or relationships of any domain (Benfares et al, 2019). These languages are RDFS (Resource Description Framework Schema) and OWL (Web Ontology Language) (Benfares, Bouzekri, Idrissi, & Abouabdellah, 2017). If, for activity "i" gets one number over activity "j," then "j" has its opposite value than "i"…”

Section: Ontologymentioning

confidence: 99%

Multi-Criteria Decision Making Semantic for Mental Healthcare

Benfares

Akhrif

Idrissi

et al. 2020

International Journal of Smart Security Technologies

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Healthcare systems play an important role in the well-being of patients; however, the diagnostic process generates a very large and varied types of data which makes the process of analyzing this data very complicated. More precisely, depression, which is one of the most common psychological disorders, contains a taxonomy of different symptoms, heterogeneous, and varied by data criteria, as confronted by clinicians to predict the degree of the disorder in patients with the aim of selecting the best treatment. To this end, the authors propose a decision architecture based on an approach that combines method ontologies, the Analytic Hierarchy Process, in the context of the prevention and monitoring of depression trends in patients.

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“…Model-based filtering is based on machine learning techniques, such as: clustering, decision trees, Bayesian networks, etc. (Benfares, El Bouzekri El Idrissi, & Abouabdellah, 2017). This approach potentially offers the benefits of both speed and scalability.…”

Section: Model-basedmentioning

confidence: 99%

“…Content-based recommendation systems are systems that recommend items similar to ones the user liked in the past (Benfares, El Bouzekri El Idrissi, & Abouabdellah, 2017). Indeed, the process consists of calculating the similarity between the attributes of a user profile with the attributes of the items in order to recommend new, interesting objects to the user.…”

Section: Content Basedmentioning

confidence: 99%

“…Their main role is to select and filter the information in a mass of information to the user in a transparent manner (Asanov, 2011). and make suggestions of topics of interest with which the system will provide the user with a list of resources according to their preferences to meet their needs (Benfares, El Bouzekri El Idrissi, & Abouabdellah, 2017). Individualized content minimizes the information load for users and provides the refined content they want, in order to make relevant decisions and understand user needs.…”

Section: An Introduction To the Recommender Systemmentioning

confidence: 99%

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Smart Collaborative Learning

Akhrif

Benfares

Idrissi

et al. 2019

International Journal of Smart Security Technologies

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Technologically enhanced learning has shifted from digital resources to smart components to afford more content, support tools, and provide learning guidance that meets a learner's needs and interests by delivering smart university services. Smart interaction is essential in smart university, it is a concept that offers new opportunities and new channels of communication between learners. This communication is reinforced by the concept of collaboration, an important factor for knowledge sharing. The current study concerns team building based on the recommendation of the most appropriate collaborator in order to make groups of learners promoting universal participation of all members of the team. The complexity of this problem requires collaborative filtering algorithms to find the potential collaborators for each learner, taking into account problem-solving as a parameter representing items of the recommendation matrix.

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Recommendation Semantic of Services In Smart City

Cited by 8 publications

References 14 publications

A case-based reasoning recommender system for sustainable smart city development

A case-based reasoning recommender system for sustainable smart city development

Multi-Criteria Decision Making Semantic for Mental Healthcare

Smart Collaborative Learning

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