A context-aware recommendation-based system for service composition in smart environments

Faieq, Soufiane; Front, Agnès; Saidi, Rajaa; Ghazi, Hassan El; Rahmani, Moulay Driss

doi:10.1007/s11761-019-00277-7

Cited by 11 publications

(5 citation statements)

References 42 publications

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“…Context-aware recommendations create better user experiences to improve performance in carrying out activities based on contexts such as location, identity, and actions of users and physical sensors on devices with applications [18], [19]. They are aware of actions, their attributes and possible activities that a user may perform by analyzing the user's behavior and nature of doing various actions [20], [21]. Data mining algorithms are classification; recommendation systems, clustering, and association rule mining are used frequently to save useful or relevant information from massive datasets [22].…”

Section: Related Work To the Proposed Methodsmentioning

confidence: 99%

Context-aware recommender system for multi-user smart home

Sobhy

Mohamed

Keshk

et al. 2023

IJECE

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<span lang="EN-US">Smart home is one of the most important applications of the internet of things (IoT). Smart home makes life simpler, easier to control, saves energy based on user’s behavior and interaction with the home appliances. Many existing approaches have designed a smart home system using data mining algorithms. However, these approaches do not consider multiusers that exist in the same location and time (which needs a complex control). They also use centralized mining algorithm, then the system’s efficiency is reduced when datasets increase. Therefore, in this paper, we firstly build a context-aware recommender system that considers multi-user’s preferences and solves their conflicts by using unsupervised algorithms to deliver useful recommendation services. Secondly, we improve smart home’s responsive using parallel computing. The results reveal that the proposed method is better than existing approaches.</span>

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Section: Related Work To the Proposed Methodsmentioning

confidence: 99%

Context-aware recommender system for multi-user smart home

Sobhy

Mohamed

Keshk

et al. 2023

IJECE

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“…This is interpreted in the systems as the situation that users are more likely to be interested in information that are already liked by other users Fig. 3 Knowledge infrastructure roles in a smart city adopted from Laurini (2013) with similar interest (Faieq et al 2019). Collaborative recommender aids sharing of knowledge and/or experiences among users who have similar interest (Alrawhani et al 2016;Alyari and Jafari Navimipour 2018).…”

Section: Collaborative Filtering Recommendersmentioning

confidence: 99%

“…Thus, contentbased recommender is based on the notion that information of an item can easily be defined into categorical data types. This approach entails a strong domain knowledge, which can be challenging to maintain (Faieq et al 2019). In the content-based recommender, some types of information such as multimedia are not easy to analyze (Alyari and Jafari Navimipour 2018).…”

Section: Content-based Recommendersmentioning

confidence: 99%

“…The hybrid recommenders include the combination of collaborative filtering and content-based recommender systems (Alrawhani et al 2016;Abu-Issa et al 2017). Hybrid recommenders aim to strengthen the recommender to address the limitations of each individual method (Faieq et al 2019). Hybrid recommender systems enhance prediction performance and also improve complexity and outlay for implementation.…”

Section: Hybrid Recommendersmentioning

confidence: 99%

“…The proposed recommender system comprises of cloud back-end layer, data analytics, context sensing, and recommended entities (potential customers and digital marketplaces). Also, Faieq et al (2019) proposed a context-aware recommendation system to provide service composition in smart environments. The researchers aimed to resolve huge number of available services and improve strong collaboration between stakeholders and providing relevant services to the users.…”

Section: Prior Recommender Systems In Smart City Domainmentioning

confidence: 99%

See 2 more Smart Citations

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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Service Re-Selection for Disruptive Events in Mobile Environments: A Heuristic Technique for Decision Support at Runtime

2023

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Modern service-based processes in mobile environments are highly complex due to the necessary spatial–temporal coordination between multiple participating users and the consideration of context information. Due to the dynamic nature of mobile environments, disruptive events occur at runtime, which require a re-selection of the planned service compositions respecting multiple users and context-awareness. Thereby, when re-selecting services the features performance, solution quality, solution robustness and alternative solutions are essential and contribute to the efficacy of service systems. This paper presents an optimization-based heuristic technique based on a stateful representation that uses a region-based approach to re-select services considering multiple users, context information and in particular disruptive events at runtime. The evaluation results, which are based on a real-world scenario from the tourism domain, show that the proposed heuristic is superior compared to competing artifacts.

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A context-aware recommendation-based system for service composition in smart environments

Cited by 11 publications

References 42 publications

Context-aware recommender system for multi-user smart home

Context-aware recommender system for multi-user smart home

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

Service Re-Selection for Disruptive Events in Mobile Environments: A Heuristic Technique for Decision Support at Runtime

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